Neural Network-based Automatic Factor Construction
This work addresses the need for more efficient and diversified factor construction in quantitative investment, though it is incremental as it builds on existing deep learning methods applied to financial data.
The paper tackled the problem of manual factor construction in quantitative finance by proposing a neural network-based framework (NNAFC) that automatically constructs financial factors, resulting in improved investment strategy metrics such as return, Sharpe ratio, and max draw-down compared to Genetic Programming.
Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Genetic Programming (GP) as an automatic feature construction tool in recent years, which builds reverse polish mathematical expressions from trading data into new factors. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures. The experiment results show that NNAFC can construct more informative and diversified factors than GP, to effectively enrich the current factor pool. For the current market, both fully connected and recurrent neural network structures are better at extracting information from financial time series than convolution neural network structures. Moreover, new factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy due to their introducing more information and diversification to the existing factor pool.