Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2021

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 × 6000 days × 56 factors.

Keywords

Quantitative Investment, Visual Analytics, Sparse Regression Factors

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Computer Graphics Forum

Volume

40

Issue

3

First Page

189

Last Page

200

ISSN

0167-7055

Identifier

10.1111/cgf.14299

Publisher

Wiley

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1111/cgf.14299

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