Decoding Financial Market: Opening Mysteries of the Indian Stock Market by Deep Learning

Over recent years, the Indian Stock Market has witnessed a surge in popularity, evident from the substantial growth in DEMAT accounts, reaching 55 million by the end of FY-21. New traders, faced with the challenge of navigating the dynamic stock market, seek predictive tools for informed decision-making. This research paper addresses this need by employing two distinct methodologies: Random Forest Classifier (RFC) and Long Short-Term Memory (LSTM) neural networks. RFC, a machine learning algorithm, is utilized to measure accuracy, F-score, precision, and recall, providing a comprehensive assessment of its predictive capabilities. On the other hand, LSTM, a deep learning approach, gauges its performance through the Root Mean Squared Error (RMSE). Through the application of these methodologies, the research aims to equip traders with effective tools for navigating the complexities of the stock market, fostering informed investment decisions in the face of market fluctuations.