The Potential of Machine Learning Techniques in Stock Market Prediction

Over the last few decades, the marketplace for stocks has shown unpredictable ups and downs, creating a potential risk for investors who could face the possibility of losing their entire investment. Understanding the importance of making accurate predictions in this uncertain situation, machine learning (ML) has become a key player. This capability allows ML to provide valuable predictive insights, helping investors make more informed decisions and potentially mitigating the risks associated with the volatile stock market. Consequently, Machine Learning based prediction tools serve as a crucial aid for investors, offering them a competitive edge and enhancing their ability to navigate the complexities of the financial markets. This article delves into the use of a machine learning techniques, employing R-squared scores as performance indicators for various models. The implemented models, including linear regression (0.999967), SVM (0.937858), Random Forest (0.999938), Gradient Boosting (0.999887), CNN (0.9995602), and LSTM (0.9990429), demonstrate high predictive capabilities. Leveraging historical data, particularly the prediction date and closing price from preceding trading sessions, our model excels in forecasting stock values using publicly available data. The model's projections consistently exhibit a slight discrepancy, typically ranging between −5 and +5 points, contrasted with real stock prices. Unlike standard stock prediction algorithms, this study employs a hybrid deep learning technique to improve prediction accuracy.