System and Method for Computer Managed Funds to Outperform Benchmarks

摘要

In order for an actively managed fund to outperform a target benchmark, it is important to seek investments with high excess returns. This invention provides a method and system for detecting potential excess returns and sorting out stocks with artificial intelligence. The method and system applies convolutional neural networks (CNN) to paired data patterns from stock and benchmark. Through deep learning, the system establishes the statistical relationship between the paired data patterns and the forward-looking excess returns. The CNN outputs represent the potential excess returns of individual stocks relative to the target benchmark. By allocating higher investment weights to stocks with higher potential excess returns, the expected return of the portfolio will be greater than the target benchmark.