iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

TL;DR

iQUANT is an interactive system that combines human expertise and computational algorithms to select and refine financial factors for quantitative investment, improving portfolio performance insights.

Contribution

The paper introduces iQUANT, a novel interactive platform that integrates human knowledge with algorithmic factor suggestions for enhanced investment decision-making.

Findings

Effective visualization aids in understanding factor and portfolio performance.

User study shows improved factor selection efficiency.

Case studies demonstrate practical investment insights.

Abstract

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.