Application of Machine Learning in Stock Price Prediction

Abstract

In the financial world, it is always difficult to predict stock prices due to the high volatility of stock prices and the noise inherent in market data. And because of some of the benefits of machine learning techniques, researchers and practitioners are increasingly adopting these techniques to increase the accuracy of predictions and support investment decisions. Therefore, this paper provides classification model (random forest, logical regression), linear regression model (linear regression, XGBoost regression), time series model (LSTM, Prophet) and other complex machine learning models. It explains the theoretical basis, key functions, and practical applications in finance and stock markets. Furthermore, it also explores the deployment of these models in specific contexts such as short-term trading guidance, portfolio rebalancing, and quantitative trading strategy development. It also addresses critical challenges associated with ML-based prediction, including data quality, model overfitting, and the need for robust risk management frameworks. Finally, this paper critically examins the essential advantages and limitations of ML in this sphere and suggests a positive outlook for future research, including the integration of unstructured data and the development of hybrid models that combine statistical and deep learning approaches.