Research on Portfolio Optimization Model based on Machine Learning Algorithm in Stock Market

Abstract

This study aims to explore portfolio optimization models based on machine learning algorithms in the stock market and evaluate their effectiveness and performance in practice. The paper combines various machine learning algorithms such as Long Short Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) to design a complex and innovative portfolio optimization model. Through training and testing using historical stock data, the paper verifies the accuracy and yield performance of the model in predicting the direction of stock price change. The experimental results show that our model performs well in forecasting accuracy and yield, and can get higher returns under the same risk, with better risk-adjusted returns. In addition, the paper also discusses the advantages of multi-algorithm combination in portfolio optimization, and analyzes the practicability and operability of the model. Our research provides a new portfolio optimization method for investors in the stock market, and has important theoretical and practical significance.