LSTM based decision support system for swing trading in stock market


Due to the highly volatile and fluctuating nature of the Indian stock market which is influenced by a number of factors including government policies, release of a company’s financial reports, investor’s sentiment, geopolitical situation, and many others, the prediction of the stock market has been a daunting task for traders. In this study, a Long Short Term Memory enforced Decision Support System is developed for swing traders to accurately analyze and predict the future stock values. The Decision support system generates a report which incorporates the predicted values of the company stock for the next 30 days and other technical indicators like MFI, relative RSI, the Support and Resistance of the stock price, five Fibonacci retracement levels, and the MACD and SIGNAL LINE analysis of the company and NIFTY industry average stock price. The trader can use the investment success score calculated in the report to augment his investment decisions. The results achieved by the proposed model in terms of Root Mean Square Error, Mean absolute error, and Mean Absolute Percentage Error are 4.13, 3.24, and 1.21 % respectively which establishes the efficacy of the proposed technique compared with the state-of-art techniques.

Knowledge-Based Systems
Shitharth Selvarajan
Shitharth Selvarajan
Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.