Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era
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paper_title: Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

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This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-the-art machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector error-correction model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor's 500 stock market index (which indicates the general state of the global economy).
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paper_title: Predicting the Price of Bitcoin Using Machine Learning

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The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.
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paper_title: How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis

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AbstractBitcoin’s emergence has the potential to pave the way for a technological revolution in financial markets. What determines its valuation is an important open question with far-reaching business and policy implications. Building on information systems and finance literature, we examine the dynamic interactions between social media and the monetary value of bitcoin using textual analysis and vector error correction models. We show that more bullish forum posts are associated with higher future bitcoin values. Interestingly, social media’s effects on bitcoin are driven primarily by the silent majority, the 95 percent of users who are less active and whose contributions amount to less than 40 percent of total messages. In addition, messages on an Internet forum, relative to tweets, have a stronger impact on future bitcoin value. Overall, our findings reveal that social media sentiment is an important predictor in determining bitcoin’s valuation, but not all social media messages are of equal impact. T...
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paper_title: Market Structure in Bitcoin Mining

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We analyze the Bitcoin protocol for electronic peer-to-peer payments and the operations that support the “blockchain” that underpins it. It is shown that that protocol maps formally into a dynamic game that is an extension of standard models of R&D racing. The model provides a technical foundation for any economic analysis of ‘proof of work’ protocols. Using the model, we demonstrate that free entry is solely responsible for determining resource usage by the system for a given reward to mining. The endogenous level of computational difficulty built into the Bitcoin protocol does not mitigate this usage and serves only to determine the time taken to process transactions. Regulating market structure will mitigate resource use highlighting the importance of identifying the benefits of competition for the operation of the blockchain.
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paper_title: The predictor impact of Web search media on Bitcoin trading volumes

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In the last decade, Web 2.0 services have been widely used as communication media. Due to the huge amount of available information, searching has become dominant in the use of Internet. Millions of users daily interact with search engines, producing valuable sources of interesting data regarding several aspects of the world. Search queries prove to be a useful source of information in financial applications, where the frequency of searches of terms related to the digital currency can be a good measure of interest in it. Bitcoin, a decentralized electronic currency, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work we studied the existing relationship between Bitcoin's trading volumes and the queries volumes of Google search engine. We achieved significant cross correlation values, demonstrating search volumes power to anticipate trading volumes of Bitcoin currency.
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paper_title: Dynamic Topic Modelling for Cryptocurrency Community Forums

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Cryptocurrencies are more and more used in official cash flows and exchange of goods. Bitcoin and the underlying blockchain technology have been looked at by big companies that are adopting and investing in this technology. The CRIX Index of cryptocurrencies http://hu.berlin/CRIX indicates a wider acceptance of cryptos. One reason for its prosperity certainly being a security aspect, since the underlying network of cryptos is decentralized. It is also unregulated and highly volatile, making the risk assessment at any given moment difficult. In message boards one finds a huge source of information in the form of unstructured text written by e.g. Bitcoin developers and investors. We collect from a popular crypto currency message board texts, user information and associated time stamps. We then provide an indicator for fraudulent schemes. This indicator is constructed using dynamic topic modelling, text mining and unsupervised machine learning. We study how opinions and the evolution of topics are connected with big events in the cryptocurrency universe. Furthermore, the predictive power of these techniques are investigated, comparing the results to known events in the cryptocurrency space. We also test hypothesis of self-fulling prophecies and herding behaviour using the results.
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paper_title: Cryptocurrency Value Formation: An Empirical Analysis Leading to a Cost of Production Model for Valuing Bitcoin.

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This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.Using cross-sectional empirical data examining 66 of the most widely used cryptocurrencies, a regression model was estimated that points to three main drivers of cryptocurrency value: the level of competition in the network of producers, the rate of unit production, and the difficulty of algorithm used to “mine” for the cryptocurrency. These amount to relative differences in the cost of production of one digital currency over another at the margin, pointing to differences in relative cost of production – electricity goes in, cryptocurrency comes out. Using that as a starting point, a no-arbitrage situation is established for Bitcoin-like cryptocurrencies followed by the formalization of a cost of production model to determine the fair value of a bitcoin.
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paper_title: The Art of Computer Virus Research and Defense

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"Of all the computer-related books I've read recently, this one influenced my thoughts about security the most. There is very little trustworthy information about computer viruses. Peter Szor is one of the best virus analysts in the world and has the perfect credentials to write this book."-Halvar Flake, Reverse Engineer, SABRE Security GmbHSymantec's chief antivirus researcher has written the definitive guide to contemporary virus threats, defense techniques, and analysis tools. Unlike most books on computer viruses, The Art of Computer Virus Research and Defense is a reference written strictly for white hats: IT and security professionals responsible for protecting their organizations against malware. Peter Szor systematically covers everything you need to know, including virus behavior and classification, protection strategies, antivirus and worm-blocking techniques, and much more.Szor presents the state-of-the-art in both malware and protection, providing the full technical detail that professionals need to handle increasingly complex attacks. Along the way, he provides extensive information on code metamorphism and other emerging techniques, so you can anticipate and prepare for future threats.Szor also offers the most thorough and practical primer on virus analysis ever published-addressing everything from creating your own personal laboratory to automating the analysis process. This book's coverage includes Discovering how malicious code attacks on a variety of platforms Classifying malware strategies for infection, in-memory operation, self-protection, payload delivery, exploitation, and more Identifying and responding to code obfuscation threats: encrypted, polymorphic, and metamorphic Mastering empirical methods for analyzing malicious code-and what to do with what you learn Reverse-engineering malicious code with disassemblers, debuggers, emulators, and virtual machines Implementing technical defenses: scanning, code emulation, disinfection, inoculation, integrity checking, sandboxing, honeypots, behavior blocking, and much more Using worm blocking, host-based intrusion prevention, and network-level defense strategies © Copyright Pearson Education. All rights reserved.
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paper_title: Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

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Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.
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paper_title: Textual sentiment in finance: A survey of methods and models

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We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and market-level behavior and performance, and vice versa. We point to what is agreed and what remains controversial. Promising directions for future research are emerging from the availability of more accurate and efficient sentiment measures resulting from increasingly sophisticated textual content analysis coupled with more extensive field-specific dictionaries. This is enabling more wide-ranging studies that use increasingly sophisticated models to help us better understand behavioral finance patterns across individuals, institutions and markets.
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paper_title: Using sentiment analysis to predict interday Bitcoin price movements

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Purpose ::: ::: ::: ::: ::: The purpose of this study is to measure the interaction between media sentiment and the Bitcoin price. Because some researchers argued that the Bitcoin value is also determined by perception of users and investors, this paper examines how. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The database of relative news articles as well as blog posts has been collected for the purpose of this research. Hence, each article has been given a sentiment score depending on the negative and positive words used in the article. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: This paper has identified that interaction between media sentiment and the Bitcoin price exists, and that there is a tendency for investors to overreact on news in a short period of time. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: While sentiment analysis of Twitter posts as a predictor of the Bitcoin price has been conducted in the past, this research does not have any analog because psycho-semantic dictionaries have not been applied earlier in the Bitcoin research.
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paper_title: The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression

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Abstract This paper provides an evaluation of the predictive performance of the volatility of three cryptocurrencies and three currencies with recognized stores of value using daily and hourly frequency data. We combined the traditional GARCH model with the machine learning approach to volatility estimation, estimating the mean and volatility equations using Support Vector Regression (SVR) and comparing to GARCH family models. Furthermore, the models’ predictive ability was evaluated using Diebold-Mariano test and Hansen’s Model Confidence Set. The analysis was reiterated for both low and high frequency data. Results showed that SVR-GARCH models managed to outperform GARCH, EGARCH and GJR-GARCH models with Normal, Student’s t and Skewed Student’s t distributions. For all variables and both time frequencies, the SVR-GARCH model exhibited statistical significance towards its superiority over GARCH and its extensions.
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paper_title: Predicting cryptocurrency price bubbles using social media data and epidemic modelling

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Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.
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paper_title: Future directions in international financial integration research - A crowdsourced perspective

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This paper is the result of a crowdsourced effort to surface perspectives on the present and future direction of international finance. The authors are researchers in financial economics who attended the INFINITI 2017 conference in the University of Valencia in June 2017 and who participated in the crowdsourcing via the Overleaf platform. This paper highlights the actual state of scientific knowledge in a multitude of fields in finance and proposes different directions for future research.
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paper_title: Can volume predict Bitcoin returns and volatility? A quantiles-based approach

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Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.
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paper_title: When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

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Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.
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paper_title: BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era

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Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies – BitCoin – have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years – digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia – and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.
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paper_title: The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin

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Cryptocurrencies, such as Bitcoin, have ignited intense discussions. Despite receiving extensive public attention, theoretical understanding is limited regarding the value of blockchain-based cryptocurrencies, as expressed in their exchange rates against traditional currencies. In this paper, we conduct a theory-driven empirical study of the Bitcoin exchange rate (against USD) determination, taking into consideration both technology and economic factors. To address co-integration in a mix of stationary and non-stationary time series, we use the autoregressive distributed lag (ARDL) model with a bounds test approach in the estimation. Meanwhile, to detect potential structural changes, we estimate our empirical model on two periods separated by the closure of Mt. Gox (one of the largest Bitcoin exchange markets). According to our analysis, in the short term, the Bitcoin exchange rate adjusts to changes in economic fundamentals and market conditions. The long-term Bitcoin exchange rate is more sensitive to economic fundamentals and less sensitive to technological factors after Mt. Gox closed. We also identify a significant impact of mining technology and a decreasing significance of mining difficulty in the Bitcoin exchange price determination. We theoretically discuss the technology and economic determinants of the Bitcoin exchange rateWe use the ARDL model with bounds test to address co-integration of a mix of stationary and non-stationary time seriesWe find Bitcoin exchange rate relates more with economic fundamentals and less with technology factors as Bitcoin evolvesWe find the impact of computational capacities on Bitcoin is decreasing as technology progresses
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paper_title: Future challenges on the use of blockchain for food traceability analysis

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Abstract The steady increase in food falsification, which has caused large economic losses and eroded consumers’ trust, has become a pressing issue for producers, researchers, governments, consumers and other stakeholders. Tracking and authenticating the food supply chain to understand provenance is critical with a view to identifying and addressing sources of contamination in the food supply chain worldwide. One way of solving traceability issues and ensuring transparency is by using blockchain technology to store data from chemical analysis in chronological order so that they are impossible to manipulate afterwards. This review examines the potential of blockchain technology for assuring traceability and authenticity in the food supply chain. It can be considered a true innovation and relevant approach to assure the quality of the third step of the analytical processes: data acquisition and management.
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paper_title: Distributed Software Dependency Management Using Blockchain

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Contemporary software deployments rely on cloud-based package managers for installation, where existing packages are installed on demand from remote code repositories. Usually frameworks or common utilities, packages increase the code reusability within the ecosystem, whilst keeping the code base small. However, disruptions in the package management services can potentially affect development and deployment workflows. Furthermore, cloud package managers have arguably an ambiguous ownership model and offer limited visibility of packages to the users. This work describes the development of a blockchain-based package control system which is decentralised, reliable, and transparent. Blockchain nodes are installed within the distributed infrastructure to provide immutability, and then a dependency graph is constructed with the help of smart contracts to trace the software provenance. Our system has been successfully tested with 4338 packages from NPM, 950 out of which are the top depended-upon packages.
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paper_title: Blockchain and law: Incompatible codes?

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Abstract Blockchain has recently joined a long line of technological innovations that have been characterised as disruptive to, and possibly even subversive of, fundamental legal principles. This article looks behind the hype to examine how blockchain might – or might not – be compatible with established legal and regulatory models. Data protection is discussed as an example of an area of law that some have claimed cannot be reconciled with blockchain. Various other conflicts are also identified and concerns about blockchain are placed in the context of wider historical debates about new technologies vs law.
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paper_title: On blockchain and its integration with IoT. Challenges and opportunities

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Abstract In the Internet of Things (IoT) vision, conventional devices become smart and autonomous. This vision is turning into a reality thanks to advances in technology, but there are still challenges to address, particularly in the security domain e.g., data reliability. Taking into account the predicted evolution of the IoT in the coming years, it is necessary to provide confidence in this huge incoming information source. Blockchain has emerged as a key technology that will transform the way in which we share information. Building trust in distributed environments without the need for authorities is a technological advance that has the potential to change many industries, the IoT among them. Disruptive technologies such as big data and cloud computing have been leveraged by IoT to overcome its limitations since its conception, and we think blockchain will be one of the next ones. This paper focuses on this relationship, investigates challenges in blockchain IoT applications, and surveys the most relevant work in order to analyze how blockchain could potentially improve the IoT.
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paper_title: Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids

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This paper tackles the problem of integrating household energy prosumers in Smart Energy Grids by analyzing a set of state-of-the-art energy forecasting techniques that allow individual or aggregated prosumers to evaluate their future energy demand and inform the Distributed System Operator (DSO) about potential grid imbalances. Thus, the DSO can perform a proactive strategy to manage the grid and avoid problems before they appear. The key element of this approach is the prediction technique, that must be accurate enough such that the resulting grid imbalances can be compensated in real-time. The paper evaluates a set of state-of-the-art statistical and Machine Learning (ML) prediction techniques, such as SARIMA, feed-forward and recurrent neural networks, support vector regression or ensemble prediction models, on real household historical energy demand logs by performing a feature selection process for each ML algorithm as to identify the best elements that influence the energy demand of a house. A set of experiments are performed on the REFIT Electrical Load Measurements data set evaluating each model’s performance with respect to the selected features. Among the evaluated algorithms, the Ensemble Prediction Model gives best prediction accuracy, showing a Mean Absolute Percentage Error (MAPE) of 14.4% followed by the SVM model with a MAPE of 15.4%.
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paper_title: Decentralizing the Stock Exchange using Blockchain An Ethereum-based implementation of the Bucharest Stock Exchange

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This paper tackles the shortcomings of the traditional centralized stock exchange systems, such as high transaction fees, centralized governance susceptible to attacks and lack of openness regarding the market actions and algorithms, by proposing an innovative architecture using blockchain to develop a decentralized stock exchange and an open continuous market. The proposed blockchain based solution solves the drawbacks of the centralized stock exchange architecture by ensuring the integrity and security of the owner’s assets and orders, self-enforcing smart agreements between parties as well as achieving democratic and reliable decisions regarding the execution and settlement of the orders through consensus algorithms. The proposed architecture uses smart contracts to enforce the validation of the owner’s rights and the correct execution and settlement of the orders, thus eliminating the need of a central authority that ensures the correctness of the stock exchange process. The solution was validated by implementing a prototype in Ethereum for a subset of rules for the Bucharest Stock Exchange. The experimental results show that the decentralized solution can offer lower transaction fees by replacing the commissions owed to brokers and central authorities with mining fees that are used to compensate the miners for their honest work in keeping the integrity of the system.
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paper_title: Optimized flexibility management enacting Data Centres participation in Smart Demand Response programs

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Abstract In this paper we address the problem of Data Centres (DCs) integration into the Smart Grid scenario by proposing a technique for scheduling and optimizing their operation allowing them to participate in Smart Demand Response programs. The technique is leveraging on DCs available flexible energy resources, on mechanisms for eliciting this latent flexibility and on an innovative electronic marketplace designed for trading energy flexibility and ancillary services. This will enact DCs to shape their energy demand to buy additional energy when prices are low and sell energy surplus when prices are high. At the same time DCs will be able to provide increased energy demand due to a large un-forecasted renewable energy production in their local grid, shed or shift energy demand over time to avoid a coincidental peak load, provide fast ramping power by turning on their backup fossil fuelled generators and injecting the energy surplus in the grid and finally provide reactive power regulation by changing their power factor. Numerical simulations results considering traces of an operational DC indicate the great potential of the proposed technique for supporting DCs participation in Smart Demand Response programs.
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paper_title: Users Views on Others – Analysis of Confused Relation-Based Terms in Social Network

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Nowadays, online social networks are used everywhere. Thus, areas such as social network analysis or recommender systems are currently very important. Roles related to relations between users, concerning influential, trusted or popular individuals are known to be crucial in these areas. While the significance of such roles is undeniable, the terms connected to these roles are not precisely specified and generally confused. In this article, we focus on the roles of users connected to terms trust, reputation, influence and popularity in the scope of social network analysis and social recommendations. We analyze existing works using these roles in order to compare and contrast their interpretations. We emphasize the most important features that the definitions of these notions should include and make the comparative analysis of the most often confused terms: trust vs reputation, and influence vs popularity. We also present the notions global classification concerning their abstract level, define the terms and distinguish them from one another.
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paper_title: Maximizing the spread of influence through a social network

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Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.
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paper_title: Finding news-topic oriented influential twitter users based on topic related hashtag community detection

paper_content:

Recently, more and more users would like to collect and provide information about news topics in Twitter, which is one of the most popular microblogging services. Virtual communities defined by hashtags in Twitter are created for exchanging information about the news topic. Finding influential Twitter users in these communities related to a news topic would help us understand why some opinions are popular, and get valuable and reliable information for the news topic. In this paper, we propose a new approach to detect news-topic-related user communities defined by hashtags based on characteristic co-occurrence word detection. We also propose RetweetRank and MentionRank to find two types of influential Twitter users from these news-topic-related communities based on user's retweet and mention activities. Experimental results show that our characteristic co-occurrence word detection methods could detect words which are highly relevant to the news topic. RetweetRank could find influential Twitter users whose tweets about the news topic are valuable and more likely to interest others. MentionRank could find influential Twitter users who have high authority on the news topic. Our methods also outperform other related methods in evaluations.
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paper_title: Topic-sensitive PageRank

paper_content:

In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.
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paper_title: Influence analysis in social networks: A survey

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Abstract Complementary to the fancy applications of social networks, influence analysis is an indispensable technique supporting these practical applications. In recent years, this emerging research branch has obtained significant attention from both industry and academia. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. Thus, in this survey, we aim to pave a comprehensive and solid starting ground for interested readers by soliciting the latest work in this area. Firstly, we provide an overview of social networks, including definition, and types of social networks. Secondly, we present the current understanding of social influence analysis from different levels, such as its definition, properties, architecture, applications, and diffusion models. Thirdly, we discuss the evaluation metrics for social influence. Fourthly, we summarize the existing evaluation models on social influence in social networks. We further provide an overview of the existing methods for influence maximization. Finally, we discuss the problems of current algorithms and future trends from various perspectives in this field. We hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising research field.
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paper_title: TwitterRank: finding topic-sensitive influential twitterers

paper_content:

This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.
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paper_title: The Influence in Twitter: Are They Really Influenced?

paper_content:

Twitter is a popular social network service which is continuously growing. Because Twitter has become an efficient platform for advertising companies as a new vast medium, it is obvious that finding influential Twitter users and measuring their influence are important. Intuitively, users who have more followers are likely to be more influential. However, the number of followers does not necessarily mean the confidence of influence. In order to find influential users in Twitter more precisely, in this paper, we present an improvement of PageRank, which we call InterRank Inter action Rank . It considers not only the follower relationship of the network but also topical similarity between users from tweet context. By using retweet information, we verify that topical similarity indeed affects the influence of a user. Then, we compare InterRank to PageRank with an assumption that influential users are more interactive with their followers. Our comparison results show that the users found by InterRank are more interactive than those by PageRank. Overall, we believe InterRank can be an attractive alternative of PageRank in finding influential users.
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paper_title: Finding news-topic oriented influential twitter users based on topic related hashtag community detection

paper_content:

Recently, more and more users would like to collect and provide information about news topics in Twitter, which is one of the most popular microblogging services. Virtual communities defined by hashtags in Twitter are created for exchanging information about the news topic. Finding influential Twitter users in these communities related to a news topic would help us understand why some opinions are popular, and get valuable and reliable information for the news topic. In this paper, we propose a new approach to detect news-topic-related user communities defined by hashtags based on characteristic co-occurrence word detection. We also propose RetweetRank and MentionRank to find two types of influential Twitter users from these news-topic-related communities based on user's retweet and mention activities. Experimental results show that our characteristic co-occurrence word detection methods could detect words which are highly relevant to the news topic. RetweetRank could find influential Twitter users whose tweets about the news topic are valuable and more likely to interest others. MentionRank could find influential Twitter users who have high authority on the news topic. Our methods also outperform other related methods in evaluations.
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paper_title: An index to quantify an individual's scientific research output

paper_content:

I propose the index $h$, defined as the number of papers with citation number higher or equal to $h$, as a useful index to characterize the scientific output of a researcher.
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paper_title: Topic-sensitive PageRank

paper_content:

In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.
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paper_title: Social Auto-Scaling

paper_content:

Today, auto-scaling solutions are still largely reactive and are based on the load measured on existing nodes nearing a threshold or traffic forecast information provided in advance of a scheduled event. Despite these advancements, events which cause a flash flood of web traffic do not always benefit from this approach to auto-scaling because the latency of provisioning new nodes is not sufficient to prevent resource saturation. This paper proposes a novel approach to preemptive compute scaling based on the buzz of specific social network hashtags. We argue that improved scaling latency and service availability can be achieved for web services that receive intermittent as well as unexpected traffic load patterns. By combining social network monitoring, with auto-scaling frameworks, this approach can reduce the operational expense impact of over- and under-provisioning as well as the business costs of the latter. We demonstrate our approach using the AWS application suite, and demonstrate how our approach expedites the auto-scaling strategy for our use case: public transportation web sites.
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paper_title: Time-sensitive influence maximization in social networks

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One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks, the propagation of information from a node to another may incur a certain amount of time delay; moreover, the value of information may decrease over time. So not only the coverage size, but also the propagation speed matters. In this paper, we propose the Time-Sensitive Influence Maximization TSIM problem, which takes into account the time dependence of the information value. Considering the time delay aspect, we develop two diffusion models, namely the Delayed Independent Cascade model and the Delayed Linear Threshold model. We show that the TSIM problem is NP-hard under these models but the spread function is monotone and submodular. Thus, a greedy approximation algorithm can achieve a 1 − 1/e approximation ratio. Moreover, we propose two time-sensitive centrality measures and compare their performance with the greedy algorithm. We evaluate our methods on four real-world datasets. Experimental results show that the proposed algorithms outperform existing methods, which ignore the decay of information value over time.
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paper_title: Maximizing the spread of influence through a social network

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Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.
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paper_title: Reputation Prediction Using Influence Conversion

paper_content:

Currently, due to constantly increasing popularity of social media sites and thus increasing amounts of data available to study, the field of social computing has gained momentum. The research about human interactions in social networks have proven to have multiple usages in e-commerce, recommendation and others. Amongst studied topics, notions of trustworthiness and influence drawn much attention in recent years. However, these notions were studied separately and independently, with little focus on the fact that, in real life, they tend to exist simultaneously. In this paper, we focus on this novel problem and present an investigation of both influence and reputation. In particular, we propose a transition method, that uses existing influence information from social network in order to predict the collective trustworthiness of the node, or reputation. Through preliminary experiments on a real-world dataset, we demonstrate the suitability of our method.
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paper_title: Influence in time-dependent citation networks

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In this paper, we present a running influence model for evaluating the pairwise impact between communities in citation networks and define the time limitations of the model. The model improves a recent one from the literature. To compare and contrast different communities influence, we present a metric for estimating the overall running influence value of particular community. We also study the problem of the time dependency of the influence within citation networks. To conduct the community influence analysis, we perform the experiments using a real-world database. The results are compared to both real-world ranking and the state-of-the-art method. They show the adequacy of the proposed metrics and model of running influence. We also demonstrate the importance of proposed differentiation within calculation and interpretation of the influence in time-dependent citation networks.
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paper_title: In the Search of Quality Influence on a Small Scale – Micro-influencers Discovery

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Discovery and detection of different social behaviors, such as influence in on-line social networks, have drawn much focus in the current research. While there are many methods tackling the issue of influence evaluation, most of them base on the underline assumption that a large audience is indispensable for an influencer to have much impact. However, in many cases, users with smaller but highly involved audience still are highly impactful. In this work, we target a novel problem of finding micro-influencers – exactly those users that have much influence on others despite of a limited range of followers. Therefore, we propose a new concept of micro-influencers in the context of Social Network Analysis, define the notion and present a flexible method aiming to discover them. The approach is tested on two real-world datasets of Facebook [24] and Pinterest [31]. The established results are promising and demonstrate the usefulness of the micro-influencer-oriented approach for potential applications.
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paper_title: The Role of Text Pre-processing in Sentiment Analysis

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It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature.
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paper_title: Stability selection using a genetic algorithm and logistic linear regression on healthcare records

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This paper presents a Genetic Algorithm (GA) application to measuring feature importance in machine learning (ML) from a large-scale database. Too many input features may cause over-fitting, therefore a feature selection is desirable. Some ML algorithms have feature selection embedded, e.g., lasso penalized linear regression or random forests. Others do not include such functionality and are sensitive to over-fitting, e.g., unregularized linear regression. The latter algorithms require that proper features are chosen before learning. Therefore, we propose a novel stability selection (SS) approach using GA-based feature selection. The proposed SS approach iteratively applies GA on a subsample of records and features. Each GA individual represents a binary vector of selected features in the subsample. An unregularized logistic linear regression model is then trained and tested using GA-selected features through cross-validation of the subsamples. GA fitness is evaluated by area under the curve (AUC) and optimized during a GA run. AUC is assessed with an unregularized logistic regression model on multiple-subsampled healthcare records, collected under the Healthcare Cost, and Utilization Project (HCUP), utilizing the National (Nationwide) Inpatient Sample (NIS) database. Reported results show that averaging feature importance from top-4 SS and the SS using GA (GASS), improves these AUC results.
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paper_title: An Introduction to Variable and Feature Selection

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Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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paper_title: An extensive empirical study of feature selection metrics for text classification

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Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g. Information Gain) evaluated on a benchmark of 229 text classification problem instances that were gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal perspectives-accuracy, F-measure, precision, and recall-since each is appropriate in different situations. The results reveal that a new feature selection metric we call 'Bi-Normal Separation' (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text classification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focuses on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspective, BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Information Gain and Chi-Squared have correlated failures, and so they work poorly together. When choosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a member of the pair---e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin.
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paper_title: Feature Selection for Highly Skewed Sentiment Analysis Tasks

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Sentiment analysis generally uses large feature sets based on a bag-of-words approach, which results in a situation where individual features are not very informative. In addition, many data sets tend to be heavily skewed. We approach this combination of challenges by investigating feature selection in order to reduce the large number of features to those that are discriminative. We examine the performance of five feature selection methods on two sentiment analysis data sets from different domains, each with different ratios of class imbalance. Our finding shows that feature selection is capable of improving the classification accuracy only in balanced or slightly skewed situations. However, it is difficult to mitigate high skewing ratios. We also conclude that there does not exist a single method that performs best across data sets and skewing ratios. However we found that TF IDF2 can help in identifying the minority class even in highly imbalanced cases.
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paper_title: Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

paper_content:

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91p on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.
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paper_title: The Cognitive Structure of Emotions

paper_content:

1. Introduction The study of emotion Types of evidence for theories of emotion Some goals for a cognitive theory of emotion 2. Structure of the theory The organisation of emotion types Basic emotions Some implications of the emotions-as-valenced-reactions claim 3. The cognitive psychology of appraisal The appraisal structure Central intensity variables 4. The intensity of emotions Global variables Local variables Variable-values, variable-weights, and emotion thresholds 5. Reactions to events: I. The well-being emotions Loss emotions and fine-grained analyses The fortunes-of-others emotions Self-pity and related states 6. Reactions to events: II. The prospect-based emotions Shock and pleasant surprise Some interrelationships between prospect-based emotions Suspense, resignation, hopelessness, and other related states 7. Reactions to agents The attribution emotions Gratitude, anger, and some other compound emotions 8. Reactions to objects The attraction emotions Fine-grained analyses and emotion sequences 9. The boundaries of the theory Emotion words and cross-cultural issues Emotion experiences and unconscious emotions Coping and the function of emotions Computational tractability.
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paper_title: Opinion mining from noisy text data

paper_content:

The proliferation of Internet has not only led to the generation of huge volumes of unstructured information in the form of web documents, but a large amount of text is also generated in the form of emails, blogs, and feedbacks, etc. The data generated from online communication acts as potential gold mines for discovering knowledge, particularly for market researchers. Text analytics has matured and is being successfully employed to mine important information from unstructured text documents. The chief bottleneck for designing text mining systems for handling blogs arise from the fact that online communication text data are often noisy. These texts are informally written. They suffer from spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on opinion extraction from noisy text data. It is aimed at extracting and consolidating opinions of customers from blogs and feedbacks, at multiple levels of granularity. We have proposed a framework in which these texts are first cleaned using domain knowledge and then subjected to mining. Ours is a semi-automated approach, in which the system aids in the process of knowledge assimilation for knowledge-base building and also performs the analytics. Domain experts ratify the knowledge base and also provide training samples for the system to automatically gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive or negative with membership values varying from zero to one. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be aggregated at any desired level of specificity i.e. feature level or product level, user level or site level, etc. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions crawled from a set of pre-defined blogs.
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paper_title: Large-scale visual sentiment ontology and detectors using adjective noun pairs

paper_content:

We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.
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paper_title: Seeing Stars: Exploiting Class Relationships For Sentiment Categorization With Respect To Rating Scales

paper_content:

We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.
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paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
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paper_title: Characterization of the affective norms for English words by discrete emotional categories.

paper_content:

The Affective Norms for English Words (ANEW) are a commonly used set of 1,034 words characterized on the affective dimensions of valence, arousal, and dominance. Traditionally, studies of affect have used stimuli characterized along either affective dimensions or discrete emotional categories, but much current research draws on both of these perspectives. As such, stimuli that have been thoroughly characterized according to both of these approaches are exceptionally useful. In an effort to provide researchers with such a characterization of stimuli, we have collected descriptive data on the ANEW to identify which discrete emotions are elicited by each word in the set. Our data, coupled with previous characterizations of the dimensional aspects of these words, will allow researchers to control for or manipulate stimulus properties in accordance with both dimensional and discrete emotional views, and provide an avenue for further integration of these two perspectives. Our data have been archived at www.psychonomic.org/archive/.
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paper_title: Identifying And Analyzing Judgment Opinions

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In this paper, we introduce a methodology for analyzing judgment opinions. We define a judgment opinion as consisting of a valence, a holder, and a topic. We decompose the task of opinion analysis into four parts: 1) recognizing the opinion; 2) identifying the valence; 3) identifying the holder; and 4) identifying the topic. In this paper, we address the first three parts and evaluate our methodology using both intrinsic and extrinsic measures.
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paper_title: SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining

paper_content:

In this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a lexical resource publicly available for research purposes, now currently licensed to more than 300 research groups and used in a variety of research projects worldwide. Both SENTIWORDNET 1.0 and 3.0 are the result of automatically annotating all WORDNET synsets according to their degrees of positivity, negativity, and neutrality. SENTIWORDNET 1.0 and 3.0 differ (a) in the versions of WORDNET which they annotate (WORDNET 2.0 and 3.0, respectively), (b) in the algorithm used for automatically annotating WORDNET, which now includes (additionally to the previous semi-supervised learning step) a random-walk step for refining the scores. We here discuss SENTIWORDNET 3.0, especially focussing on the improvements concerning aspect (b) that it embodies with respect to version 1.0. We also report the results of evaluating SENTIWORDNET 3.0 against a fragment of WORDNET 3.0 manually annotated for positivity, negativity, and neutrality; these results indicate accuracy improvements of about 20% with respect to SENTIWORDNET 1.0.
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paper_title: Nowcasting the Bitcoin Market with Twitter Signals

paper_content:

This paper describes early work on the correlation between Bitcoin price indicators and Bitcoin-related Twitter posts. Within a timeframe of 104 days, a total of 161,200 Twitter posts containing"bitcoin"in combination with positive or negative sentiments and signals of uncertainty has been collected. Positive and negative sentiments were ex-ante filtered with the help of API queries, by using identifiers such as the words"happy, love, fun, good"or"bad, sad, unhappy"respectively. Additionally, tweets on"hope, fear"and"worry"have been collected to derive insights on market dynamics and uncertainties. Current results show a significant positive correlation of emotional tweets with intraday close prices and trading volumes of Bitcoin. Data suggests: When Bitcoin transactions fly high, emotions fly high on Twitter, too. Another observation is that a balanced ratio of negative and positive sentiments positively correlates with the trading volume and thus may designate Twitter as place that reflects the"speculative momentum"within the market. Summarizing, the microblogging platform Twitter may be interpreted as a virtual trading floor that heats up with virtual emotions and reflects market movements.
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paper_title: Large-scale World Cup 2014 outcome prediction based on Tumblr posts

paper_content:

With the 2014 FIFA World Cup kicking o on June 12th, billions of fans across the world have turned their attention toward host country Brazil to root for their teams. Soccer (or football, if you prefer) fans are loud; you need only remember the last World Cup’s infamous vuvuzelas for a demonstration. But fans are not only loud in stadiums. They also make their voices heard across social media. And though you may assume these fans are just blowing their vuvuzelas into the social abyss, if you listen closely, you will discover a treasure trove of data, including an answer to the most important question of all: "Who will win?". In this paper we use Tumblr posts collected during 4 months prior to the start of the World Cup to predict the outcome of every game. We describe the prediction algorithm as well as the analysis of the performance results, including comparison of the predictions of several competing methods.
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paper_title: Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

paper_content:

This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-the-art machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector error-correction model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor's 500 stock market index (which indicates the general state of the global economy).
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paper_title: Volatility estimation for Bitcoin: A comparison of GARCH models

paper_content:

We explore the optimal conditional heteroskedasticity model with regards to goodness-of-fit to Bitcoin price data. It is found that the best model is the AR-CGARCH model, highlighting the significance of including both a short-run and a long-run component of the conditional variance.
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paper_title: Correlating financial time series with micro-blogging activity

paper_content:

We study the problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks. Specifically, we collect messages related to a number of companies, and we search for correlations between stock-market events for those companies and features extracted from the micro-blogging messages. The features we extract can be categorized in two groups. Features in the first group measure the overall activity in the micro-blogging platform, such as number of posts, number of re-posts, and so on. Features in the second group measure properties of an induced interaction graph, for instance, the number of connected components, statistics on the degree distribution, and other graph-based properties. We present detailed experimental results measuring the correlation of the stock market events with these features, using Twitter as a data source. Our results show that the most correlated features are the number of connected components and the number of nodes of the interaction graph. The correlation is stronger with the traded volume than with the price of the stock. However, by using a simulator we show that even relatively small correlations between price and micro-blogging features can be exploited to drive a stock trading strategy that outperforms other baseline strategies.
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paper_title: Generative and Discriminative Text Classification with Recurrent Neural Networks

paper_content:

We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerful than their bag-of-words ancestors (e.g., they account for conditional dependencies across words in a document), they have higher asymptotic error rates than discriminatively trained RNN models. However we also find that generative models approach their asymptotic error rate more rapidly than their discriminative counterparts---the same pattern that Ng&Jordan (2001) proved holds for linear classification models that make more naive conditional independence assumptions. Building on this finding, we hypothesize that RNN-based generative classification models will be more robust to shifts in the data distribution. This hypothesis is confirmed in a series of experiments in zero-shot and continual learning settings that show that generative models substantially outperform discriminative models.
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paper_title: Techniques and applications for sentiment analysis

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The main applications and challenges of one of the hottest research areas in computer science.
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paper_title: Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

paper_content:

This paper describes early work trying to predict stock market indicators such as Dow Jones, NASDAQ and S&P 500 by analyzing Twitter posts. We collected the twitter feeds for six months and got a randomized subsample of about one hundredth of the full volume of all tweets. We measured collective hope and fear on each day and analyzed the correlation between these indices and the stock market indicators. We found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX. It therefore seems that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.
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paper_title: Twitter mood predicts the stock market

paper_content:

Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%. Index Terms—stock market prediction — twitter — mood analysis.
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paper_title: Is All That Talk Just Noise ? The Information Content of Internet Stock Message Boards

paper_content:

Financial press reports claim that Internet stock message boards can move markets. We study the effect of more than 1.5 million messages posted on Yahooe Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average and the Dow Jones Internet Index. Bullishness is measured using computational linguistics methods. "Wall Street Journal" news stories are used as controls. We find that stock messages help predict market volatility. Their effect on stock returns is statistically significant but economically small. Consistent with Harris and Raviv (1993) , disagreement among the posted messages is associated with increased trading volume. Copyright 2004 by The American Finance Association.
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paper_title: Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

paper_content:

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.
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paper_title: Correlating S&P 500 stocks with Twitter data

paper_content:

Twitter is a widely used online social media. One important characteristic of Twitter is its real-time nature. In this paper, we investigate whether the daily number of tweets that mention Standard & Poor 500 (S&P 500) stocks is correlated with S&P 500 stock indicators (stock price and traded volume) at three different levels, from the stock market to industry sector and individual company stocks. We further apply a linear regression with exogenous input model to predict stock market indicators, using Twitter data as exogenous input. Our preliminary results demonstrate that daily number of tweets is correlated with certain stock market indicators at each level. Furthermore, it appears that Twitter is helpful to predict stock market. Specifically, at the stock market level, we find that whether S&P 500 closing price will go up or down can be predicted more accurately when including Twitter data in the model.
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paper_title: Predicting Bitcoin price fluctuation with Twitter sentiment analysis

paper_content:

Programmatically deriving sentiment has been the topic of many a thesis: it’s application in analyzing 140 character sentences, to that of 400-word Hemingway sentences; the methods ranging from nai ...
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paper_title: Using sentiment analysis to predict interday Bitcoin price movements

paper_content:

Purpose ::: ::: ::: ::: ::: The purpose of this study is to measure the interaction between media sentiment and the Bitcoin price. Because some researchers argued that the Bitcoin value is also determined by perception of users and investors, this paper examines how. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The database of relative news articles as well as blog posts has been collected for the purpose of this research. Hence, each article has been given a sentiment score depending on the negative and positive words used in the article. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: This paper has identified that interaction between media sentiment and the Bitcoin price exists, and that there is a tendency for investors to overreact on news in a short period of time. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: While sentiment analysis of Twitter posts as a predictor of the Bitcoin price has been conducted in the past, this research does not have any analog because psycho-semantic dictionaries have not been applied earlier in the Bitcoin research.
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paper_title: Foundations Of Neuro-Fuzzy Systems

paper_content:

From the Publisher: ::: Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.
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paper_title: The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression

paper_content:

Abstract This paper provides an evaluation of the predictive performance of the volatility of three cryptocurrencies and three currencies with recognized stores of value using daily and hourly frequency data. We combined the traditional GARCH model with the machine learning approach to volatility estimation, estimating the mean and volatility equations using Support Vector Regression (SVR) and comparing to GARCH family models. Furthermore, the models’ predictive ability was evaluated using Diebold-Mariano test and Hansen’s Model Confidence Set. The analysis was reiterated for both low and high frequency data. Results showed that SVR-GARCH models managed to outperform GARCH, EGARCH and GJR-GARCH models with Normal, Student’s t and Skewed Student’s t distributions. For all variables and both time frequencies, the SVR-GARCH model exhibited statistical significance towards its superiority over GARCH and its extensions.
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paper_title: When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

paper_content:

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.
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paper_title: BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era

paper_content:

Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies – BitCoin – have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years – digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia – and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.
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paper_title: Cryptocurrencies Transactions Advisor Using a Genetic Mamdani-type Fuzzy Rules Based System

paper_content:

Cryptocurrencies prices forecasting is a complex theme due to the chaotic market behavior and the influence of external events. Therefore, inference models should offer, in addition to a satisfying accuracy, reasonable interpretability, so that investors can decide based on their own knowledge. However, many studies in this subject focus on model accuracy and leave much to be desired in terms of simplicity and interpretability. This work proposes the use of Mamdani interpretable fuzzy inference models for forecasting cryptocurrency price variation. For that, a genetic algorithm to optimize models accuracy is employed, limiting the quantity of rules and antecedents arbitrarily. A set of infeasible rules had to be discarded, in order to generate interesting models, that produce a relevant amount of trades. Data from Kraken exchange were utilized for training, validation and results assessment. Results have shown that, for the cryptocurrencies with the highest validation performances, there are gains in comparison to the simple currency appreciation. Using the interpretable aspect of the models, it should be possible to obtain even higher profits.
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paper_title: A Forex trading system based on a genetic algorithm

paper_content:

In this paper, a genetic algorithm will be described that aims at optimizing a set of rules that constitute a trading system for the Forex market. Each individual in the population represents a set of ten technical trading rules (five to enter a position and five others to exit). These rules have 31 parameters in total, which correspond to the individuals' genes. The population will evolve in a given environment, defined by a time series of a specific currency pair. The fitness of a given individual represents how well it has been able to adapt to the environment, and it is calculated by applying the corresponding rules to the time series, and then calculating the ratio between the profit and the maximum drawdown (the Stirling ratio). Two currency pairs have been used: EUR/USD and GBP/USD. Different data was used for the evolution of the population and for testing the best individuals. The results achieved by the system are discussed. The best individuals are able to achieve very good results in the training series. In the test series, the developed strategies show some difficulty in achieving positive results, if you take transaction costs into account. If you ignore transaction costs, the results are mostly positive, showing that the best individuals have some forecasting ability.
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paper_title: When Can Social Media Lead Financial Markets?

paper_content:

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.
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paper_title: Stability selection using a genetic algorithm and logistic linear regression on healthcare records

paper_content:

This paper presents a Genetic Algorithm (GA) application to measuring feature importance in machine learning (ML) from a large-scale database. Too many input features may cause over-fitting, therefore a feature selection is desirable. Some ML algorithms have feature selection embedded, e.g., lasso penalized linear regression or random forests. Others do not include such functionality and are sensitive to over-fitting, e.g., unregularized linear regression. The latter algorithms require that proper features are chosen before learning. Therefore, we propose a novel stability selection (SS) approach using GA-based feature selection. The proposed SS approach iteratively applies GA on a subsample of records and features. Each GA individual represents a binary vector of selected features in the subsample. An unregularized logistic linear regression model is then trained and tested using GA-selected features through cross-validation of the subsamples. GA fitness is evaluated by area under the curve (AUC) and optimized during a GA run. AUC is assessed with an unregularized logistic regression model on multiple-subsampled healthcare records, collected under the Healthcare Cost, and Utilization Project (HCUP), utilizing the National (Nationwide) Inpatient Sample (NIS) database. Reported results show that averaging feature importance from top-4 SS and the SS using GA (GASS), improves these AUC results.
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paper_title: A black-box discrete optimization benchmarking (BB-DOB) pipeline survey: taxonomy, evaluation, and ranking

paper_content:

This paper provides a taxonomical identification survey of classes in discrete optimization challenges that can be found in the literature including a proposed pipeline for benchmarking, inspired by previous computational optimization competitions. Thereby, a Black-Box Discrete Optimization Benchmarking (BB-DOB) perspective is presented for the BB-DOB@GECCO Workshop. It is motivated why certain classes together with their properties (like deception and separability or toy problem label) should be included in the perspective. Moreover, guidelines on how to select significant instances within these classes, the design of experiments setup, performance measures, and presentation methods and formats are discussed.
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paper_title: Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution

paper_content:

This paper present short-term combined economic and emission hydrothermal optimization, addressing total fuel costs and emissions minimization. This paper uses the fuel cost function with valve-point effect, which increases the degree of optimization problem difficulty. The optimal balance between the addressed objectives, that conflict with each other, can be obtained with appropriate hydro and thermal generation schedules. A surrogate differential evolution is applied in order to satisfy 24-h system demand and final states of hydro power plant reservoirs by minimized total fuel costs and emissions. This paper proposes a novel master–slave model optimization algorithm, where the optimal thermal schedules are obtained within the slave model. The data obtained from the slave model are saved into a matrix, which serves as a surrogate model for a master model, where the hydrothermal optimization with all objectives and constraints is conducted by using a parallel self-adaptive differential evolution algorithm. In order to show the effectiveness of the proposed method, different case studies are used: economic load scheduling, economic emission scheduling, and combined economic emission scheduling. The proposed method is verified on a model consisting of four hydro power plants and three thermal power plants.
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paper_title: Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review

paper_content:

In this paper we provide a review of the current state of research on Portfolio Management with the support of Multiobjective Evolutionary Algorithms (MOEAs). Second we present a methodological framework for conducting a comprehensive literature review on the Multiobjective Evolutionary Algorithms (MOEAs) for the Portfolio Management. Third, we use this framework to gain an understanding of the current state of the MOEAs for the Portfolio Management research field and fourth, based on the literature review, we identify areas of concern with regard to MOEAs for the Portfolio Management research field.
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paper_title: Portfolio rebalancing with respect to market psychology in a fuzzy environment: A case study in Tehran Stock Exchange

paper_content:

Abstract While a vast amount of literature shows that psychological factors are major pricing determinants, portfolio optimization models ignore the emotional aspects of financial markets. Accordingly, this paper presents a two-stage portfolio rebalancing method to integrate mean-variance theory with market psychology. At the first stage, the psychological state of market participants is translated into a set of criteria to evaluate stocks, and then, in a fuzzy environment, the process of ratiocination used by technical analysts is simulated to assess the status of these criteria and determine under- and overvaluation possibilities of stocks. At the second stage, a fuzzy programming approach utilizes the calculated possibilities to revise an existing portfolio considering investor profile, transaction costs, and risk-free rate of return. An empirical study using the obtained data from Tehran Stock Exchange is employed to validate the designed method and compare it against several other investment strategies, including Buy-and-Hold strategy and a conventional portfolio rebalancing model. The results show that the proposed fuzzy method responds appropriately to the psychological component of the market. In addition, for all investor profiles, the recommended strategy completely outperforms the market and the remaining strategies.
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paper_title: Multiple criteria decision aiding for finance: An updated bibliographic survey

paper_content:

Abstract Finance is a popular field for applied and methodological research involving multiple criteria decision aiding (MCDA) techniques. In this study we present an up-to-date bibliographic survey of the contributions of MCDA in financial decision making, focusing on the developments during the past decade. The survey covers all main areas of financial modeling as well as the different methodological approaches in MCDA and its connections with other analytical fields. On the basis of the survey results, we discuss the contributions of MCDA in different areas of financial decision making and identify established and emerging research topics, as well as future opportunities and challenges.
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paper_title: A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

paper_content:

The coinciding development of multiobjective evolutionary algorithms (MOEAs) and the emergence of complex problem formulation in the finance and economics areas has led to a mutual interest from both research communities. Since the 1990s, an increasing number of works have thus proposed the application of MOEAs to solve complex financial and economic problems, involving multiple objectives. This paper provides a survey on the state-of-the-art of research, reported in the specialized literature to date, related to this framework. The taxonomy chosen here makes a distinction between the (widely covered) portfolio optimization problem and the other applications in the field. In addition, potential paths for future research within this area are identified.
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paper_title: On the increasing importance of multiple criteria decision aid methods for portfolio selection

paper_content:

AbstractIn 1952, Markowitz published his famous paper on portfolio selection that transformed the field of finance. Although over 65 years have passed since then, the mean-variance model remains today the predominant model in portfolio selection. Having endured many criticisms over this period, the one that has perhaps been the most persistent is the fact that mainstream mean-variance theory is unable to accommodate additional criteria beyond expected return and variance. With investment decision-making having become more complex, this is a real problem as many problems with additional criteria exist and are only increasing in number and importance. In this paper, we review the papers that have been published that apply methods and procedures in an exact (as opposed to evolutionary) sense to address problems in portfolio selection with criteria beyond mean and variance. We also analyse the methodologies that allow the solution of the problem in a multiple criteria context, thus extending the features of t...
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paper_title: Portfolio Diversification across Cryptocurrencies

paper_content:

Abstract Utilizing the empirical data of ten major cryptocurrencies, this article examines the investablitiy and role of diversification in cryptocurrency market, and evaluates the out-of-sample performance of commonly used asset allocation models across cryptocurrencies. We show that portfolio diversification across different cryptocurrencies can significantly improve the investment results. We also find robust evidence that the maximum utility model dominates the out-of-sample utility, although none of the models can consistently beat the naive 1/N portfolio in Sharpe ratio.
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paper_title: Optimal vs naïve diversification in cryptocurrencies

paper_content:

This paper contributes to the literature on cryptocurrencies by examining the performance of naive (1/N) and optimal (Markowitz) diversification in a portfolio of four popular cryptocurrencies. We employ weekly data with weekly rebalancing and show there is very little to select between naive diversification and optimal diversification. Our results hold for different levels of risk-aversion and an alternative estimation window.
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paper_title: Success history applied to expert system for underwater glider path planning using differential evolution

paper_content:

Abstract This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from literature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is targeting higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms existing literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision-makers, when they use the improved UGPP expert system yielding better trajectories.
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paper_title: Cryptocurrency-portfolios in a mean-variance framework

paper_content:

Abstract We apply the Markowitz mean-variance framework in order to assess risk-return benefits of cryptocurrency-portfolios. Using daily data of the 500 most capitalized cryptocurrencies for the time span 1/1/2015 to 12/31/2017, we relate risk and return of different mean-variance portfolio strategies to single cryptocurrency investments and two benchmarks, the naively diversified portfolio and the CRIX. In an out-of-sample analysis accounting for transaction cost we find that combining cryptocurrencies enriches the set of ‘low’-risk cryptocurrency investment opportunities. In terms of the Sharpe ratio and certainty equivalent returns, the 1/N-portfolio outperforms single cryptocurrencies and more than 75% of mean-variance optimal portfolios.
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paper_title: A survey of swarm intelligence for portfolio optimization: Algorithms and applications

paper_content:

Abstract In portfolio optimization (PO), often, a risk measure is an objective to be minimized or an efficient frontier representing the best tradeoff between return and risk is sought. In order to overcome computational difficulties of this NP-hard problem, a growing number of researchers have adopted swarm intelligence (SI) methodologies to deal with PO. The main PO models are summarized, and the suggested SI methodologies are analyzed in depth by conducting a survey from the recent published literature. Hence, this study provides a review of the SI contributions to PO literature and identifies areas of opportunity for future research.
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paper_title: Self-adaptive control parameters' randomization frequency and propagations in differential evolution

paper_content:

Abstract This paper presents insight into an adaptation and self-adaptation mechanism within differential evolution, covering not only how but moreover – when this mechanism generates new values for control parameters, focusing on the iteration-temporal randomness of the self-adaptive control parameters. In particular, this randomness is controlled by a randomness level parameter, which influences the control parameters values׳ dynamics and their propagation through suitable individuals׳ improvement contributions during ellitistic selection. Thereby, the randomness level parameter defines the chaotic behavior of self-adaptive control parameter values׳ instances. A Differential Evolution (DE) algorithm for Real Parameter Single Objective Optimization is utilized as an application of this mechanism, to analyze the impact of the randomness level parameter as used inside the evolutionary algorithm parameter adaptation and control mechanism, yielding statistically significant different algorithm performances and ranks on different randomness level parameter values. Moreover, the impacts of different randomness configurations on the number of improvements, improvement scales, and adaptation frequencies, are shown, in order to present a deeper insight into the influences and causes using different randomness level parameter configurations, to present the influence of randomization frequency on propagation stability. Since DE variant algorithms with the mechanism of control parameters self-adaptation are widely applied, this study might help in increasing the performances of these different variants and their applications.
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paper_title: Bitcoin: Medium of Exchange or Speculative Assets?

paper_content:

Bitcoin is defined as digital money within a decentralized peer-to-peer payment network. It is a hybrid between precious metals with intrinsic value and fiat currency backed by a monetary authority. This paper analyses the question of whether Bitcoin is currency or investment and more specifically, what is its current usage and what usage will prevail in the future given its characteristics. We analyse the statistical properties of Bitcoin and find that it is essentially uncorrelated with traditional asset classes such as stocks and bonds. The analysis of transaction data of Bitcoin accounts shows that bitcoins are mainly used as an investment and not as an alternative currency. Currently Bitcoin is “small” relative to the size of other asset classes and thus does not pose an immediate risk for monetary, financial or economic stability.
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paper_title: Distance based parameter adaptation for Success-History based Differential Evolution

paper_content:

Abstract This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions.
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paper_title: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

paper_content:

A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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paper_title: Price discovery of cryptocurrencies: Bitcoin and beyond

paper_content:

Academic research on cryptocurrencies is almost exclusively directed towards Bitcoin. We extend existing literature by performing various tests on efficiency of several cryptocurrencies and additionally link efficiency to measures of liquidity. Cryptocurrencies become less predictable / inefficient as liquidity increases.
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paper_title: Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution

paper_content:

This paper present short-term combined economic and emission hydrothermal optimization, addressing total fuel costs and emissions minimization. This paper uses the fuel cost function with valve-point effect, which increases the degree of optimization problem difficulty. The optimal balance between the addressed objectives, that conflict with each other, can be obtained with appropriate hydro and thermal generation schedules. A surrogate differential evolution is applied in order to satisfy 24-h system demand and final states of hydro power plant reservoirs by minimized total fuel costs and emissions. This paper proposes a novel master–slave model optimization algorithm, where the optimal thermal schedules are obtained within the slave model. The data obtained from the slave model are saved into a matrix, which serves as a surrogate model for a master model, where the hydrothermal optimization with all objectives and constraints is conducted by using a parallel self-adaptive differential evolution algorithm. In order to show the effectiveness of the proposed method, different case studies are used: economic load scheduling, economic emission scheduling, and combined economic emission scheduling. The proposed method is verified on a model consisting of four hydro power plants and three thermal power plants.
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paper_title: Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review

paper_content:

In this paper we provide a review of the current state of research on Portfolio Management with the support of Multiobjective Evolutionary Algorithms (MOEAs). Second we present a methodological framework for conducting a comprehensive literature review on the Multiobjective Evolutionary Algorithms (MOEAs) for the Portfolio Management. Third, we use this framework to gain an understanding of the current state of the MOEAs for the Portfolio Management research field and fourth, based on the literature review, we identify areas of concern with regard to MOEAs for the Portfolio Management research field.
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paper_title: Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

paper_content:

NSGA II outperforms SPEA 2 with wider fronts and better relations return-risk.Technical Analysis shows added value to trading when compared to Buy & Hold.Bollinger Bands is the strategy of election with strong positive net results. Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) within portfolio optimization. In addition, when used with four TA based strategies relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.
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paper_title: Use of heuristic rules in evolutionary methods for the selection of optimal investment portfolios

paper_content:

A novel hybrid algorithm that combines evolutionary algorithms, quadratic programming, and a specially devised pruning heuristic is proposed for the selection of cardinality- constrained optimal portfolios. The framework used is the standard Markowitz mean-variance formulation for portfolio selection with constraints of practical interest, such as minimum and maximum investments per asset and/or on groups of assets. The use of cardinality constraints transforms portfolio selection into an NP-hard mixed-integer quadratic optimization problem that is difficult to solve by standard methods. An implementation of the algorithm that employs a genetic algorithm with a set representation, an appropriately defined mutation operator and Random Assortment Recombination for crossover (RAR-GA) is compared with implementations using various estimation of distribution algorithms (EDAs). Without the pruning heuristic, RAR-GA is superior to the implementations with EDAs in terms of both accuracy and efficiency. The incorporation of the pruning heuristic leads to a significant decrease in computation times and makes EDAs competitive with RAR-GA.
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paper_title: MNFS-FPM: A novel memetic neuro-fuzzy system based financial portfolio management

paper_content:

Portfolio management consists of deciding what assets to include in a portfolio given the investor's objectives and changing market and economic conditions. The always difficult selection process includes identifying which assets to purchase, how much, and when. This paper presents a novel memetic neuro-fuzzy system for financial portfolio management (MNFS-FPM) which emulates the thinking process of a rational investor and generates the optimal portfolio from a collection of assets based on a chosen investment style. The system consists mainly of two modules: the generic self-organizing fuzzy neural network realizing Yager inference (GenSoFNN-Yager), to predict the expected return of each asset, and a memetic algorithm using simplex local searches (MA-NM/SMD) to determine the optimal investment weight allocation for all assets in the portfolio. Experimental results on Dow Jones industrial average (DJIA) stocks show that the proposed system yields better performance compared to that of existing financial models: statistical mean-variance analysis and capital asset pricing model (CAPM).
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paper_title: The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

paper_content:

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.
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paper_title: An integrated multi-objective Markowitz-DEA cross-efficiency model with fuzzy returns for portfolio selection problem

paper_content:

We combine Markowitz and DEA cross-efficiency model for portfolio selection.We model uncertainty by considering the asset returns as trapezoidal fuzzy numbers.We use the NSGA-II algorithm to solve the model.We apply the proposed model for 52 firms listed in stock exchange market of Iran. In this paper, a novel multi objective model is proposed for portfolio selection. The proposed model incorporates the DEA cross-efficiency into Markowitz mean-variance model and considers return, risk and efficiency of the portfolio. Also, in order to take uncertainty in proposed model, the asset returns are considered as trapezoidal fuzzy numbers. Due to the computational complication of the proposed model, the second version of non-dominated sorting genetic algorithm (NSGA-II) is applied. To illustrate the performance of our model, the model is implemented for 52 firms listed in stock exchange market of Iran and the results are analyzed. The results show that the proposed model is suitable in compared with Markowitz and DEA models due to considering return, risk and efficiency, simultaneously.
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paper_title: Genetic algorithms for portfolio selection problems with minimum transaction lots

paper_content:

Conventionally, portfolio selection problems are solved with quadratic or linear programming models. However, the solutions obtained by these methods are in real numbers and difficult to implement because each asset usually has its minimum transaction lot. Methods considering minimum transaction lots were developed based on some linear portfolio optimization models. However, no study has ever investigated the minimum transaction lot problem in portfolio optimization based on Markowitz’ model, which is probably the most well-known and widely used. Based on Markowitz’ model, this study presents three possible models for portfolio selection problems with minimum transaction lots, and devises corresponding genetic algorithms to obtain the solutions. The results of the empirical study show that the portfolios obtained using the proposed algorithms are very close to the efficient frontier, indicating that the proposed method can obtain near optimal and also practically feasible solutions to the portfolio selection problem in an acceptable short time. One model that is based on a fuzzy multi-objective decision-making approach is highly recommended because of its adaptability and simplicity.
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paper_title: Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm

paper_content:

Heuristic algorithms strengthen researchers to solve more complex and combinatorial problems in a reasonable time. Markowitz's Mean-Variance portfolio selection model is one of those aforesaid problems. Actually, Markowitz's model is a nonlinear (quadratic) programming problem which has been solved by a variety of heuristic and non-heuristic techniques. In this paper a portfolio selection model which is based on Markowitz's portfolio selection problem including three of the most important limitations is considered. The results can lead Markowitz's model to a more practical one. Minimum transaction lots, cardinality constraints (both of which have been presented before in other researches) and market (sector) capitalization (which is proposed in this research for the first time as a constraint for Markowitz model), are considered in extended model. No study has ever proposed and solved this expanded model. To solve this mixed-integer nonlinear programming (NP-Hard), a corresponding genetic algorithm (GA) is utilized. Computational study is performed in two main parts; first, verifying and validating proposed GA and second, studying the applicability of presented model using large scale problems.
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paper_title: Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection

paper_content:

Graphical abstractDisplay Omitted HighlightsWe consider a constrained three-objective optimization portfolio selection problem.We solve the problem by means of evolutionary multi-objective optimization.New mutation, crossover and reparation operators are designed for this problem.They are tested in several algorithms for a data set from the Spanish stock market.Results for two performance metrics reveal the effectiveness of the new operators. In this paper, we consider a recently proposed model for portfolio selection, called Mean-Downside Risk-Skewness (MDRS) model. This modelling approach takes into account both the multidimensional nature of the portfolio selection problem and the requirements imposed by the investor. Concretely, it optimizes the expected return, the downside-risk and the skewness of a given portfolio, taking into account budget, bound and cardinality constraints. The quantification of the uncertain future return on a given portfolio is approximated by means of LR-fuzzy numbers, while the moments of its return are evaluated using possibility theory. The main purpose of this paper is to solve the MDRS portfolio selection model as a whole constrained three-objective optimization problem, what has not been done before, in order to analyse the efficient portfolios which optimize the three criteria simultaneously. For this aim, we propose new mutation, crossover and reparation operators for evolutionary multi-objective optimization, which have been specially designed for generating feasible solutions of the cardinality constrained MDRS problem. We incorporate the operators suggested into the evolutionary algorithms NSGAII, MOEA/D and GWASF-GA and we analyse their performances for a data set from the Spanish stock market. The potential of our operators is shown in comparison to other commonly used genetic operators and some conclusions are highlighted from the analysis of the trade-offs among the three criteria.
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paper_title: A survey of swarm intelligence for portfolio optimization: Algorithms and applications

paper_content:

Abstract In portfolio optimization (PO), often, a risk measure is an objective to be minimized or an efficient frontier representing the best tradeoff between return and risk is sought. In order to overcome computational difficulties of this NP-hard problem, a growing number of researchers have adopted swarm intelligence (SI) methodologies to deal with PO. The main PO models are summarized, and the suggested SI methodologies are analyzed in depth by conducting a survey from the recent published literature. Hence, this study provides a review of the SI contributions to PO literature and identifies areas of opportunity for future research.
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paper_title: Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization

paper_content:

This work presents particle swarm optimization (PSO), a collaborative population-based meta-heuristic algorithm for solving the Cardinality Constraints Markowitz Portfolio Optimization problem (CCMPO problem). To our knowledge, an efficient algorithmic solution for this nonlinear mixed quadratic programming problem has not been proposed until now. Using heuristic algorithms in this case is imperative. To solve the CCMPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the proposed PSO is much more robust and effective than existing PSO algorithms, especially for low-risk investment portfolios. In most cases, the PSO outperformed genetic algorithm (GA), simulated annealing (SA), and tabu search (TS).
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paper_title: Cryptocurrency portfolio management with deep reinforcement learning

paper_content:

Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Backtest trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 months' periods. Some recently published portfolio selection strategies are also used to perform the same back-tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.
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paper_title: Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing

paper_content:

Portfolio optimization by GA is a problem that has recently received a lot of attention. However, most works in this area have so far ignored the effects of cost on Portfolio Optimization, and haven't directly addressed the problem of portfolio management (continuous optimization of a portfolio over time). In this work, we use the Euclidean Distance between the portfolio selection in two consecutive time periods as measure of cost, and the objective sharing method to balance the goals of maximizing returns and minimizing distance over time. We also improve the GA method by adding genetic material from previous runs into the new population (seeding). We experiment our method on historical monthly data from the NASDAQ and NIKKEI indexes, and obtain a better result than pure GA, defeating the index under non-bubble market conditions.
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paper_title: Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization

paper_content:

Abstract To be successful in financial market trading it is necessary to correctly predict future market trends. Most professional traders use technical analysis to forecast future market prices. In this paper, we present a new hybrid intelligent method to forecast financial time series, especially for the Foreign Exchange Market (FX). To emulate the way real traders make predictions, this method uses both historical market data and chart patterns to forecast market trends. First, wavelet full decomposition of time series analysis was used as an Adaptive Network-based Fuzzy Inference System (ANFIS) input data for forecasting future market prices. Also, Quantum-behaved Particle Swarm Optimization (QPSO) for tuning the ANFIS membership functions has been used. The second part of this paper proposes a novel hybrid Dynamic Time Warping (DTW)-Wavelet Transform (WT) method for automatic pattern extraction. The results indicate that the presented hybrid method is a very useful and effective one for financial price forecasting and financial pattern extraction.
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paper_title: Herding behaviour in cryptocurrencies

paper_content:

This study examines the presence of herding behaviour in the cryptocurrency market. The latter is the outcome of mass collaboration and imitation. Results from the static model suggest no significant herding. However, the presence of structural breaks and nonlinearities in the data series suggests applying a static model is not appropriate. Accordingly, we conduct a rolling-window analysis, and those results point to significant herding behaviour, which varies over time. Using a logistic regression, we find that herding tends to occur as uncertainty increases. Our findings induce useful insights related to portfolio and risk management, trading strategies, and market efficiency.
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paper_title: Predicting cryptocurrency price bubbles using social media data and epidemic modelling

paper_content:

Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.
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Title: Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Section 1: Related Work
Description 1: Review recent work related to the objective of Sentiment Analysis for cryptocurrencies, focusing on impacts like value prediction and other underlying principles.

Section 2: Background: Cryptocurrencies and Blockchain
Description 2: Provide an overview of cryptocurrencies and blockchain technology, including their unique characteristics and applications.

Section 3: Blockchain Technologies for Energy Markets
Description 3: Discuss the application of blockchain technologies in the energy market, including smart energy systems and decentralized management.

Section 4: Specific Challenge A: Influence on Social Media
Description 4: Explain how influence on social media impacts cryptocurrencies and the importance of identifying influential users.

Section 5: Influential Users' Discovery
Description 5: Detail methods for discovering influential users who significantly impact social networks and, subsequently, cryptocurrency trends.

Section 6: Influence Diffusion and Influence Maximization
Description 6: Describe approaches for understanding how information spreads in networks and the strategies to maximize this diffusion.

Section 7: General Model
Description 7: Introduce general models for evaluating influence among users on social networks.

Section 8: Specific Challenge B: Social Media Feeds' Annotation and Dictionary Definition
Description 8: Discuss strategies for annotating social media feeds and defining sentiment dictionaries tailored for financial applications.

Section 9: Specific Challenge C: Filtering Tweets
Description 9: Outline the methods for preprocessing Twitter data to enhance sentiment analysis accuracy.

Section 10: Specific Challenge D: Sentiment Analysis
Description 10: Explain different approaches to sentiment analysis, including knowledge-based, statistical, and hybrid techniques.

Section 11: Specific Challenge E: Technical Analysis and Forecasting
Description 11: Examine the application of sentiment analysis in forecasting, particularly for predicting cryptocurrency fluctuations.

Section 12: Specific Challenge F: Ranking ICOs
Description 12: Address the challenges in ranking Initial Coin Offerings (ICOs) and the role of sentiment analysis.

Section 13: Specific Challenge G: Multi-objective Cryptocurrency Portfolio Selection
Description 13: Discuss strategies for portfolio selection in cryptocurrency investments, focusing on multi-objective optimization.

Section 14: Optimization Algorithms
Description 14: Review various metaheuristic optimization algorithms used in portfolio management.

Section 15: Specific Challenge H: Investment Approaches with the New ICO Sentiment Indicator
Description 15: Explore investment strategies that incorporate new sentiment indicators and address cryptocurrency-specific characteristics.

Section 16: Conclusion
Description 16: Summarize the surveyed perspectives and suggest future implementations and experimental results based on the presented guidelines.