Feed-Forward Neural Network Model: A Non-Linear Optimization Technique for Share Price Prediction of TCS Index
Author(s): Sachin Kamley, Dinesh Kumar Sahu, Varsha Namdeo
Authors Affiliations:
1Research Scholar (M. Tech), Department of IT, SRK University, Bhopal (M.P.), India
2Associate Professor, Department of IT, SRK University, Bhopal (M.P.), India
3Professor, Department of IT, SRK University, Bhopal (M.P.), India
DOIs:10.2015/IJIRMF/202510023 | Paper ID: IJIRMF202510023Abstract: These days, a lot of attention has been paid to the stock market, which is also connected to our nation's economic prosperity. Both the economy and the lives of millions of individuals can be impacted by changes in stock prices. Investor confidence will always increase when markets rise, and the opposite effect on society has always occurred when markets collapse. When investing in the stock market, people always base their decisions on observation and employ a variety of instruments and strategies to learn about market trends, such as reading newspapers, watching television, etc. Because short-term predictions carry less danger than long-term investments, the majority of people are interested in them. The Feed Forward Network Network (FFNN) approach to stock market analysis and day-by-day short-term forecasting on stock data is the primary focus of this article. Investors and stock users would benefit from this research study by choosing the best share to maximize profits.
Sachin Kamley, Dinesh Kumar Sahu, Varsha Namdeo (2025); Feed-Forward Neural Network Model: A Non-Linear Optimization Technique for Share Price Prediction of TCS Index, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-10, Pp.166-174. Available on – https://www.ijirmf.com/