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 × 6000 days × 56 factors.

中文翻译：

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iQUANT：使用稀疏回归因子的交互式量化投资

使用金融因素的基于模型的投资正在发展成为定量投资的主要方法。主要挑战在于对超额市场回报的有效因素的选择。现有的方法，无论是手动选择因素还是应用特征选择算法，都无法协调人类知识和计算能力。本文介绍了 iQUANT，这是一个交互式量化投资系统，可帮助股票交易者从算法模型建议的初始建议中快速发现有前景的金融因素，并对投资组合构成的因素和股票进行联合细化。我们与专业交易员密切合作，汇集“好”因子的经验特征，并提出有效的可视化设计来说明金融因子、股票投资组合及其相互作用的集体表现。我们使用由 3000 只股票组成的真实股市数据集，通过正式的用户研究、两个案例研究和专家访谈来评估 iQUANT× 6000 天× 56 个因子。