Research on investment returns prediction based on machine learning

Abstract

With the development of the Internet and the continuous improvement of the information disclosure mechanism of the securities market, individual investors have more and more ways to obtain investment information, including company research reports from analysts. However, analysts have mixed qualifications and publish a huge number of reports. For retail investors, how to analyze and distinguish these reports and make informed investment decisions based on them has become the key to profiting in a securities market where information is asymmetrical and risks and opportunities coexist. This is a priority that every retail investor should prioritize. In recent years, machine learning has become increasingly widely used in the financial sector, especially in the prediction of return on investment. In this work, a most advanced machine learning model Transformer is used, which is widely used in a variety of scenarios due to its advantages in processing sequence data. The Transformer model can effectively process and analyze large-scale financial time series data through the self-attention mechanism to capture subtle fluctuations and potential correlations in the market. We designed a Transformer-based architecture to combine multi-source data such as market macroeconomic indicators, stock trading data, and social sentiment analysis for comprehensive learning. Experimental results show that the model significantly improves the accuracy of investment return prediction and provides investors with efficient analysis tools.