Reinforcement learning based predictive analytics framework for survival in stock market. (12th October 2021)

• Record Type: Journal Article
• Authors: Mittal, Shruti; Nagpal, Chander Kumar
• Is Part Of: International journal of intelligent engineering informatics. Volume 9:Number 3(2021)
• Page Start: 294
• Page End: 327
• Publication Date: 2021-10-12
• Subjects: predictive analytics -- statistical learning -- machine learning -- stock market predictions -- reinforcement learning -- fuzzy sets and logic -- finite state machine -- fuzzy rule base -- stock fundamental -- stock technical analysis -- single value decomposition; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

Contemporary research in stock market domain is limited to forecasting of the stock price from one day to one week. Such small period predictions cannot be of much help for continuous gainful survival in the stock market. In fact, there has to be predictive analytics framework which analyses the current situation in the holistic manner and provides the appropriate advice for selling/buying/no action along with the quantity resulting in significant gain for the user/investor. The proposed framework generates various reinforcement signals by applying statistical and machine learning techniques on historical data and studies their impact on the stock prices by analysing future data. The outcome of the process has been used to generate rewards, through the use of fuzzy logic, for various actions in a given state of the environment. Fully automated implementation of the proposed framework can help both institutional and common investor in taking the rational decision.