Stock Price Forecasting using Reinforcement Learning and Sentimental Analysis

Computer Engineering

Our project focuses on predicting stock prices through machine learning models. Leveraging historical stock data and financial indicators, we employ algorithms like Linear Regression and Neural Networks. Feature engineering and time-series analysis enhance accuracy. The project aims to aid investors with informed decision-making by providing reliable stock price forecasts. Furthermore, the project incorporates time-series analysis to capture inherent patterns and trends in stock prices. The evaluation of model performance involves metrics such as Mean Squared Error and R-squared to assess the accuracy and reliability of the predictions. This Stock Price Prediction project not only contributes to the growing field of financial technology but also provides a valuable tool for market participants seeking more informed decision-making processes in the volatile world of stock trading. This project is not just a composition of algorithms; it's a bridge between data and decision-making, a symphony of technology and investment insight. We aim to provide investors with the tools to navigate the intricate dance of the stock market with newfound confidence, transforming the once-chaotic melody into a harmonious path towards financial success. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price information.

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