RankFIRST:通过排列股票时间序列进行因子投资的可视化分析。

IF 4.7 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Huijie Guo, Meijun Liu, Bowen Yang, Ye Sun, Huamin Qu, Lei Shi
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引用次数: 0

摘要

在量化投资时代,基于因子的投资模型被广泛用于构建股票投资组合。这些模型通过一系列金融因子(如市场贝塔系数和公司规模)来解释个股的表现。在工业领域,开放式投资平台允许在线构建基于因子的模型,但对最终用户的工程专业知识提出了很高的要求。最先进的可视化系统集成了整个因子投资管道,但并不能直接满足领域用户在构建投资组合时对因子和股票进行排序的核心要求。当前的模型缺乏可解释性,降低了其在股票投资者中的可信度。为了填补因子投资在股票时间序列建模、排名和可视化方面的空白,我们设计并实现了一个可视化分析系统,即 RankFIRST。该系统内置了对已建立的因子集合和横截面回归模型的支持,适合人工解读。根据股票投资所需的良好因子特征,引入了分层斜率图设计。此外,还发明了一种新颖的烟花图,将著名的蜡烛图扩展到股票时间序列。我们利用最近 30 年中国股市的完整数据对系统进行了评估。案例研究和受控用户评估表明,与股指被动投资和现有股市可视化分析工具相比,我们的系统在因子投资方面更具优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RankFIRST: Visual Analysis for Factor Investment By Ranking Stock Timeseries.

In the era of quantitative investment, factor-based investing models are widely adopted in the construction of stock portfolios. These models explain the performance of individual stocks by a set of financial factors, e.g., market beta and company size. In industry, open investment platforms allow the online building of factor-based models, yet set a high bar on the engineering expertise of end-users. State-of-the-art visualization systems integrate the whole factor investing pipeline, but do not directly address domain users' core requests on ranking factors and stocks for portfolio construction. The current model lacks explainability, which downgrades its credibility with stock investors. To fill the gap in modeling, ranking, and visualizing stock time series for factor investment, we designed and implemented a visual analytics system, namely RankFIRST. The system offers built-in support for an established factor collection and a cross-sectional regression model viable for human interpretation. A hierarchical slope graph design is introduced according to the desired characteristics of good factors for stock investment. A novel firework chart is also invented extending the well-known candlestick chart for stock time series. We evaluated the system on the full-scale Chinese stock market data in the recent 30 years. Case studies and controlled user evaluation demonstrate the superiority of our system on factor investing, in comparison to both passive investing on stock indices and existing stock market visual analytics tools.

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来源期刊
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics 工程技术-计算机:软件工程
CiteScore
10.40
自引率
19.20%
发文量
946
审稿时长
4.5 months
期刊介绍: TVCG is a scholarly, archival journal published monthly. Its Editorial Board strives to publish papers that present important research results and state-of-the-art seminal papers in computer graphics, visualization, and virtual reality. Specific topics include, but are not limited to: rendering technologies; geometric modeling and processing; shape analysis; graphics hardware; animation and simulation; perception, interaction and user interfaces; haptics; computational photography; high-dynamic range imaging and display; user studies and evaluation; biomedical visualization; volume visualization and graphics; visual analytics for machine learning; topology-based visualization; visual programming and software visualization; visualization in data science; virtual reality, augmented reality and mixed reality; advanced display technology, (e.g., 3D, immersive and multi-modal displays); applications of computer graphics and visualization.
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