RESUMEN
The fund investment industry heavily relies on the expertise of fund managers, who bear the responsibility of managing portfolios on behalf of clients. With their investment knowledge and professional skills, fund managers gain a competitive advantage over the average investor in the market. Consequently, investors prefer entrusting their investments to fund managers rather than directly investing in funds. For these investors, the primary concern is selecting a suitable fund manager. While previous studies have employed quantitative or qualitative methods to analyze various aspects of fund managers, such as performance metrics, personal characteristics, and performance persistence, they often face challenges when dealing with a large candidate space. Moreover, distinguishing whether a fund manager's performance stems from skill or luck poses a challenge, making it difficult to align with investors' preferences in the selection process. To address these challenges, this study characterizes the requirements of investors in selecting suitable fund managers and proposes an interactive visual analytics system called FMLens. This system streamlines the fund manager selection process, allowing investors to efficiently assess and deconstruct fund managers' investment styles and abilities across multiple dimensions. Additionally, the system empowers investors to scrutinize and compare fund managers' performances. The effectiveness of the approach is demonstrated through two case studies and a qualitative user study. Feedback from domain experts indicates that the system excels in analyzing fund managers from diverse perspectives, enhancing the efficiency of fund manager evaluation and selection.
RESUMEN
Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs.
RESUMEN
The research on massive open online courses (MOOCs) data analytics has mushroomed recently because of the rapid development of MOOCs. The MOOC data not only contains learner profiles and learning outcomes, but also sequential information about when and which type of learning activities each learner performs, such as reviewing a lecture video before undertaking an assignment. Learning sequence analytics could help understand the correlations between learning sequences and performances, which further characterize different learner groups. However, few works have explored the sequence of learning activities, which have mostly been considered aggregated events. A visual analytics system called ViSeq is introduced to resolve the loss of sequential information, to visualize the learning sequence of different learner groups, and to help better understand the reasons behind the learning behaviors. The system facilitates users in exploring learning sequences from multiple levels of granularity. ViSeq incorporates four linked views: the projection view to identify learner groups, the pattern view to exhibit overall sequential patterns within a selected group, the sequence view to illustrate the transitions between consecutive events, and the individual view with an augmented sequence chain to compare selected personal learning sequences. Case studies and expert interviews were conducted to evaluate the system.
RESUMEN
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 s Portfolio, 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.
RESUMEN
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.