Statistical Approach in Indian Capital Market through Quantitative Modeling of Quarterly Financial Metrics Using Deep Learning
Keywords:
Quarterly results, Fundamental Analysis, Stock Price Prediction, Technical Analysis, Deep LearningAbstract
The use of computing power in stock market analysis has been a popular field of research for investors and retailers seeking to maximize their profits from the market. However, there is limited research on the relationship between quarterly financial results and future stock price movements for companies listed on the National Stock Exchange in India. This study aims to fill this knowledge gap by examining this relationship and analyzing the impact of key technical and fundamental parameters on future stock prices. Data scraping techniques were used to collect quarterly results and stock price data, and the analysis showed that the proposed model provided an average profit of 142% over a three-year period, with an annual profit of 34.7%. The neural network model achieved a 62.9% accuracy on the test dataset. Improvement opportunities exist for higher accuracy. The experimental results demonstrate that the proposed model can play a vital role in stock price prediction and could be useful for investment decision-making. Overall, this study provides valuable insights into the impact of stock fundamentals on stock prices and could be a valuable resource for investors and retailers seeking to maximize their profits from the stock market.
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