iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

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dc.contributor.authorYue, Xuanwuen_US
dc.contributor.authorGu, Qiaoen_US
dc.contributor.authorWang, Deyunen_US
dc.contributor.authorQu, Huaminen_US
dc.contributor.authorWang, Yongen_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:01:34Z
dc.date.available2021-06-12T11:01:34Z
dc.date.issued2021
dc.description.abstractThe 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 x 6000 days x 56 factors.en_US
dc.description.number3
dc.description.sectionheadersMachine Learning and Explainable AI
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.14299
dc.identifier.issn1467-8659
dc.identifier.pages189-200
dc.identifier.urihttps://doi.org/10.1111/cgf.14299
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14299
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectVisualization design and evaluation methods
dc.subjectInformation visualization
dc.titleiQUANT: Interactive Quantitative Investment Using Sparse Regression Factorsen_US
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