Applications of Neuro Deep Learning Models in Predictive Data Analytics for the Movements and Trends of the Indian Stock Market: Financial Data Mining, Nonlinearity, and Quantum Finance - SIGO, MARXIA OLI; SELVAM, MURUGESAN; VENKATESWAR, SANKARAN; MANIAM, BALASUNDRAM; SUBRAMANIAM, GEETHA; KANNAIAH, DESTI (Journal of Ecohumanism, 2024)
The financial world of today is high-frequency data-driven and characterized by the application and use of information technology for better business development and decision-making. The price movements of stock markets are mainly influenced by micro and macroeconomic variables, legal frameworks, and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms, based on historical prices. Achieving accuracy for forecasting the market trend has become difficult due to the prevalence of stochastic behaviour in the stock market and volatility in the stock prices. This paper analyzes the stochastic movement pattern of the highly volatile thirty individual company stocks (in terms of market capitalization) of BSE-Sensex, using the Neuro Deep Learning method, a model of deep learning approach. The findings of the study would help the investors, to make rational and well-informed investment decisions, and to optimize the returns by investing in the most valuable stocks of listed corporate enterprises.