Abstract
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.
Highlights
Analyzing and forecasting future stock prices has attracted much research interest
The results showed that the average Root Mean Squared Relative Error (RMSRE) performance achieved by Long Short Term Memory (LSTM)-FD, General Adversary Networks (GAN)-FD, Recurrent Neural Network (RNN)-LBL, and Sentiment Aware Stock Price Forecasting (SASPF) was 0.0197, 0.00885, 0.00675, and 0.005465, respectively
The results showed that the average Direction Prediction Accuracy (DPA) performance achieved by LSTM-FD, GAN-FG, RNN-LBL, and SASPF model was 0.6423, 0.68585, 0.7822, and 0.80805, respectively
Summary
Analyzing and forecasting future stock prices has attracted much research interest. Success in stock trading totally relies on determining the right time to sell or buy stocks. Despite the advances in technical analysis, it is difficult to accurately predict stock prices and trading points in financial markets, mostly because of the volatile investors’ sentiments during uncertainties. Most studies use technical analysis, which includes older stock volumes and prices, in order to demonstrate trading profits and predict future stock market prices [6]. Research studies focused on discovering native parameters, mostly used web data such as Social Networking Services (SNS) [7], investor’s sentiments [8], news articles [9], or search engines [10], and explained that search query frequencies and investors’ sentiments from SNS platforms provide vital information for predicting upcoming stock prices, especially during global uncertainties such as the recent CoViD-19 pandemic. Reliance Industries Limited (RIL) stock prices were continuously growing as Facebook and Google obtained certain percentage of its stake during the CoViD-19 pandemic
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering, Technology & Applied Science Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
