Open AccessSentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model
Author(s) -
Uma Gurav,
S. Kotrappa
Publication year - 2020
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.3805
Subject(s) - computer science , recurrent neural network , sentiment analysis , technical analysis , stock (firearms) , stock market , trading strategy , stock price , econometrics , artificial intelligence , artificial neural network , machine learning , financial economics , economics , series (stratigraphy) , biology , mechanical engineering , paleontology , horse , engineering
Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.