iQUANT: Interactive quantitative investment using sparse regression factors
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...
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sg-smu-ink.sis_research-77472022-04-26T07:19:17Z iQUANT: Interactive quantitative investment using sparse regression factors YUE, Xuanwu GU, Qiao WANG, Deyun QU, Huamin WANG, Yong 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. 2021-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6744 info:doi/10.1111/cgf.14299 https://ink.library.smu.edu.sg/context/sis_research/article/7747/viewcontent/21_EuroVis_iQuant.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Quantitative Investment Visual Analytics Sparse Regression Factors Databases and Information Systems Graphics and Human Computer Interfaces |
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Quantitative Investment Visual Analytics Sparse Regression Factors Databases and Information Systems Graphics and Human Computer Interfaces YUE, Xuanwu GU, Qiao WANG, Deyun QU, Huamin WANG, Yong iQUANT: Interactive quantitative investment using sparse regression factors |
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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. |
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YUE, Xuanwu GU, Qiao WANG, Deyun QU, Huamin WANG, Yong |
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YUE, Xuanwu GU, Qiao WANG, Deyun QU, Huamin WANG, Yong |
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YUE, Xuanwu |
title |
iQUANT: Interactive quantitative investment using sparse regression factors |
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iQUANT: Interactive quantitative investment using sparse regression factors |
title_full |
iQUANT: Interactive quantitative investment using sparse regression factors |
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iQUANT: Interactive quantitative investment using sparse regression factors |
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iQUANT: Interactive quantitative investment using sparse regression factors |
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iquant: interactive quantitative investment using sparse regression factors |
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Institutional Knowledge at Singapore Management University |
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2021 |
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https://ink.library.smu.edu.sg/sis_research/6744 https://ink.library.smu.edu.sg/context/sis_research/article/7747/viewcontent/21_EuroVis_iQuant.pdf |
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