iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors
This work addresses the problem of factor selection in quantitative investment for equity traders by integrating human knowledge with computational models, offering an incremental improvement over existing methods.
The paper tackles the challenge of selecting effective financial factors for quantitative investment by introducing iQUANT, an interactive system that combines algorithmic recommendations with human expertise to refine factors and stocks for portfolio composition. It was evaluated with a real dataset of 3000 stocks over 6000 days and 56 factors, showing improved factor selection and portfolio performance through user studies and expert interviews.
The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of "good" factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks times 6000 days times 56 factors.