Stock Market Analysis and Price Prediction

This Study presents a new approach to stock price analysis and prediction by combining several methodologies: fundamental analysis, logistic regression, technical analysis through the k-Nearest Neighbors (KNN) algorithm, and price forecasting through the application of LSTM neural networks. The first approach was to use fundamental analysis to determine intrinsic values for stocks based on financial health and market conditions. Market trends and patterns will be captured by performing technical analysis on KNN algorithms that identify similarities in historical data points. Core to the price prediction, it would depend on the LSTM networks which are powerful in modeling time-series data and could be able to capture complex and non- linear relationships found in the movements of the stock price. All the strengths mentioned above will have a great deal in improving the accuracy of the predictions of this proposed model. Experimental results indicate that the integrated model is much more reliable and precise than the traditional stock prediction methods. Therefore, this study is aimed at examining the potential for using machine learning and advanced statistical techniques to enhance forecasting capabilities in stock market analysis to enable investors and analysts with better decision-making tools through more powerful analysis.