Publications
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iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors
To appear
Author: Xuanwu Yue, Qiao Gu, Deyun Wang, Huamin Qu, and Yong Wang
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 times 6000 days times 56 factors.
TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups
IEEE Transactions on Visualization & Computer Graphics, 2020
Author: Yating Lin*, Kamkwai Wong*, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, and Qinghua Zheng
[ abstract ] [ url ] [ preprint ]
Tax evasion is a serious economic problem for many countries, as it can undermine the government' s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they failed to support the analysis and exploration of the uprising related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data, and interviews with domain experts.
TradAO: A Visual Analytics System for Trading Algorithm Optimization
IEEE Visualization Conference (VIS), 2020
Author: Ka Wing Tsang, Haotian Li, Fuk Ming Lam, Yifan Mu, Yong Wang, and Huamin Qu
[ abstract ] [ url ] [ preprint ]
With the wide applications of algorithmic trading, it has become critical for traders to build a winning trading algorithm to beat the market. However, due to the lack of efficient tools, traders mainly rely on their memory to manually compare the algorithm instances of a trading algorithm and further select the best trading algorithm instance for the real trading deployment. We work closely with industry practitioners to discover and consolidate user requirements and develop an interactive visual analytics system for trading algorithm optimization. Structured expert interviews are conducted to evaluateTradAOand a representative case study is documented for illustrating the system effectiveness. To the best of our knowledge, previous financial data visual analyses have mainly aimed to assist investment managers in investment portfolio analysis but have neglected the need of traders in developing trading algorithms for portfolio execution.TradAOis the first visual analytics system that assists users in comprehensively exploring the performances of a trading algorithm with different parameter settings.
sPortfolio: Stratified Visual Analysis of Stock Portfolios
IEEE Transactions on Visualization & Computer Graphics, 2019
Author: Xuanwu Yue, Jiaxin Bai, Qinhan Liu, Yiyang Tang, Abishek Puri, Ke Li, and Huamin Qu
Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present sPortfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.
BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence
IEEE Transactions on Visualization & Computer Graphics, 2018
Author: Xuanwu Yue, Xinhuan Shu, Xinyu Zhu, Xinnan Du, Zheqing Yu, Dimitrios Papadopoulos, and Siyuan Liu
The emerging prosperity of cryptocurrencies, such as Bitcoin, has come into the spotlight during the past few years. Cryptocurrency exchanges, which act as the gateway to this world, now play a dominant role in the circulation of Bitcoin. Thus, delving into the analysis of the transaction patterns of exchanges can shed light on the evolution and trends in the Bitcoin market, and participants can gain hints for identifying credible exchanges as well. Not only Bitcoin practitioners but also researchers in the financial domains are interested in the business intelligence behind the curtain. However, the task of multiple exchanges exploration and comparisons has been limited owing to the lack of efficient tools. Previous methods of visualizing Bitcoin data have mainly concentrated on tracking suspicious transaction logs, but it is cumbersome to analyze exchanges and their relationships with existing tools and methods. In this paper, we present BitExTract, an interactive visual analytics system, which, to the best of our knowledge, is the first attempt to explore the evolutionary transaction patterns of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange versus client. In particular, BitExTract summarizes the evolution of the Bitcoin market by observing the transactions between exchanges over time via a massive sequence view. A node-link diagram with ego-centered views depicts the trading network of exchanges and their temporal transaction distribution. Moreover, BitExTract embeds multiple parallel bars on a timeline to examine and compare the evolution patterns of transactions between different exchanges. Three case studies with novel insights demonstrate the effectiveness and usability of our system.
Towards Easy Comparison of Local Businesses Using Online Reviews
Computer Graphics Forum, 2018
Author: Yong WANG, Hammad HALEEM, Conglei SHI, Yanhong WU, Xun ZHAO, Siwei FU, and Huamin QU
With the rapid development of e‐commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E‐Comp, a carefully‐designed visual analytics system based on online reviews, to help customers compare local businesses at different levels of details. More specifically, intuitive glyphs overlaid on maps are designed for quick candidate selection. Grouped Sankey diagram visualizing the rating difference by common customers is chosen for more reliable comparison of two businesses. Augmented word cloud showing adjective‐noun word pairs, combined with a temporal view, is proposed to facilitate in‐depth comparison of businesses in terms of different time periods, rating scores and features. The effectiveness and usability of E‐Comp are demonstrated through a case study and in‐depth user interviews.
A Survey on Visual Analysis of Financial Data
Author: Xuanwu Yue
Market participants and businesses continually spare no efforts towards making a smarter decision during the past decades. From the perspective of data statistics and data analysis, the progress that based on the enormous financial data has drawn a lot of attention not only in the industry but also the academic. However, owing to the high diversity, large volume, and unstructured characteristic, the visual analysis has great potential among data exploration progress. The tasks summarized by financial practitioners provide a more clear direction for computer science fields. However, there are limited visual analysis systems developed to solve those tasks efficiently. Proper summarization combining main functions and applications in financial areas should be emphasized to get an overview of state-of-art work and future potential research fields. In this survey, we first introduce the background and motivation of visual analysis on financial data and give a comprehensive review of typical tasks like anomaly detection, predictive analysis, correlative analysis, and application in FinTech. Finally, we conclude the survey with a discussion of future research directions.
FinaVistory: Using Narrative Visualization to Explain Social and Economic Relationships in Financial News
International Conference on Big Data and Smart Computing (BigComp), 2016
Author: Yeuk-Yin CHAN and Huamin QU
TrendFocus: Visualization of trends in financial news with indicator sets
International Conference on Big Data and Smart Computing (BigComp), 2014
Author: Yik-Wai NG and Huamin Qu
Although many current visualization approaches to time-series data can show a good general trend of a topic or theme over time, most cannot help people gain a deeper understanding of key issues. They may illustrate how some key issues come up and disappear from time to time, but they typically provide limited information about relationships of interest. Financial and economic analysts are often interested in how financial or economic performance indicators interplay or change over time. With a set of well-defined and commonly used equations or indicators in the domain of Economics and Finance, we can refine our visualization by keeping track of these variables. In this paper, we make use of a line plot layout to track some of these indicators and leverage word clouds to reveal how these indicators are affected by the changes in social and economic environments.
