3 ways big data is changing financial trading

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Finance and trading rely on accurate inputs into business decision-making models. Traditionally numbers were crunched by humans and decisions made based on inferences drawn from calculated risks and trends. Today, this functionality is usurped by computers. They can compute at massive scale, and draw from a multitude of sources to come to more accurate conclusions almost instantaneously.

There are three ways big data is influencing financial trading, and here they are.

1. Leveraging big data analytics in financial models

Financial analytics is no longer just the examination of prices and price behaviour but integrates the principles that affect prices, social and political trends and the elucidation of support and opposition levels.

Big data analytics can be used in predictive models to estimate the rates of return and probably outcomes on investments. Increasing access to big data results in more precise predictions and thus the ability to more effectively mitigate the inherent risks associated with financial trading.

High frequency trading has been used quite successfully up until now, with machines trading independently of human input. However, the computing timeframe habitually puts this method out of the game as literally seconds are of the essence with this type of trade and big data usually means increasing processing time. The paradigm is changing though, as traders realise the value and advantages of accurate extrapolations they achieve with big data analytics.

2. Real time analytics

Algorithmic trading is the buzzword in finance at the moment. Machine learning is enabling computers to make human-like decisions, executing trades at rapid speeds and frequencies that people cannot. The business archetype incorporates the best possible prices, traded at specific times and reduces manual errors that arise due to behavioural influences.

Real-time analytics has the potential to improve the investing power of HFT firms and individuals alike, as the insights gleaned by algorithmic analysis has levelled the playing field providing all with access to powerful information.

The power of algorithmic trading lies in the almost limitless capabilities. Structured and unstructured data can be used and thus social media, stock market information and news analysis can be used to make intuitive judgements. This situational sentiment analysis is highly valuable as the stock market is an easily influenced archetype.

3. Machine learning

The full potential of this technology hasn’t yet been realised and the prospects for the application of these innovations are immeasurable. Machine learning enables computers to actually learn and make decisions based on new information by learning from past mistakes and employing logic. In this way, these techniques can deliver supremely accurate perceptions. Although, the technology is still developing, the possibilities are promising. This particular avenue of research removes the human emotional response from the model and makes decisions based on information without bias.   

The burgeoning role of big data in financial trading

There is inordinate potential for computers to take over this sector in the near future. Big data allows more information to be fed into a system that thrives on knowledge of all possible influencers. The big data analytical revolution makes it possible to trade more accurately and informedly; impacting dramatically on how financial transactions are executed.

As big data continues to reform the framework of various industries, the financial sector is adopting big data analytics to maintain the competitive advantage in the trading environment. It is doubtful that it will be very long before this technology becomes a mainstream necessity for financial institutions.
