Stock Movement and Volatility Prediction from Tweets, Macroeconomic
  Factors and Historical Prices
Shengkun Wang YangXiao Bai Taoran Ji Kaiqun Fu Linhan Wang Chang-Tien Lu
Predicting stock market is vital for investors and policymakers, acting as a
barometer of the economic health. We leverage social media data, a potent
source of public sentiment, in tandem with macroeconomic indicators as
government-compiled statistics, to refine stock market predictions. However,
prior research using tweet data for stock market prediction faces three
challenges. First, the quality of tweets varies widely. While many are filled
with noise and irrelevant details, only a few genuinely mirror the actual
market scenario. Second, solely focusing on the historical data of a particular
stock without considering its sector can lead to oversight. Stocks within the
same industry often exhibit correlated price behaviors. Lastly, simply
forecasting the direction of price movement without assessing its magnitude is
of limited value, as the extent of the rise or fall truly determines
profitability. In this paper, diverging from the conventional methods, we
pioneer an ECON. The framework has following advantages: First, ECON has an
adept tweets filter that efficiently extracts and decodes the vast array of
tweet data. Second, ECON discerns multi-level relationships among stocks,
sectors, and macroeconomic factors through a self-aware mechanism in semantic
space. Third, ECON offers enhanced accuracy in predicting substantial stock
price fluctuations by capitalizing on stock price movement. We showcase the
state-of-the-art performance of our proposed model using a dataset,
specifically curated by us, for predicting stock market movements and
volatility.