The Multifactor Quantitative Investment Model Based on Association Rule Mining and Machine Learning

Abstract

The security information database has accumulated a large amount of historical data due to the continuing development of the securities market. People are concerned about how to fully utilize these data to investigate the securities market’s law. In the financial field, financial asset pricing is a major issue. To some extent, the size of the return is determined by the difference between asset prices and their intrinsic value. The total global investment scale of quantitative funds will surpass 20 billion yuan by the end of 2021. Global asset management firms have turned to quantitative funds as their most important investment tool. Quantitative investment applies a specific investment idea to a specific model by creating specific indicators and parameters and then executes the investment strategy, greatly increasing the breadth and depth of investment. The goal of investors is to understand risk and maximize returns on investment. Researchers and investors alike value quantitative investment because of its scientific and efficient operation. In quantitative stock selection, a multifactor stock selection model is a critical tool for building a portfolio. This paper builds a multifactor investment strategy based on the relevant factors of corporate finance and valuation, selects the portfolio, and calculates the excess return using a machine learning classification algorithm.