THESIS
2019
xv, 110 pages : illustrations ; 30 cm
Abstract
Visualization techniques have been widely utilized to facilitate the analysis in different financial
fields, such as trading markets, risk assessment, anomaly transaction detection, asset
management. Visual analysis techniques could contribute to systematic intelligence generation
and strategy extraction for domain practitioners and overcome the perceptual barrier for the
public. However, previous works mainly focus on the novel visual representation or internal
system which relies on high domain knowledge. In this thesis, we focused on the visual analysis
of financial transaction data to generate intelligence, strategies, and patterns that are more
easily accepted by the public and end-users, rather than being limited to the professionals with
strong domain knowledge. We ground...[
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Visualization techniques have been widely utilized to facilitate the analysis in different financial
fields, such as trading markets, risk assessment, anomaly transaction detection, asset
management. Visual analysis techniques could contribute to systematic intelligence generation
and strategy extraction for domain practitioners and overcome the perceptual barrier for the
public. However, previous works mainly focus on the novel visual representation or internal
system which relies on high domain knowledge. In this thesis, we focused on the visual analysis
of financial transaction data to generate intelligence, strategies, and patterns that are more
easily accepted by the public and end-users, rather than being limited to the professionals with
strong domain knowledge. We grounded our study on specific applications: cryptocurrency
exchange and quantitative investment.
The first research problem focuses on the evolutionary transaction patterns of cryptocurrency
exchanges. Delving into the analysis of the transaction patterns of exchanges can shed
light on the evolution and trends in the cryptocurrency market, and participants can gain hints
for identifying credible exchanges as well. Specifically, we present a visual analytics system
named BitExTract, which is the first attempt to explore the evolutionary transaction patterns
of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange
versus client. Our second focusing area is quantitative investment. The essence of quantitative investment is the multi-factor model, one that explains the relationship between the risk and
return of equities. The challenge is to develop visualization tools that can effectively analyze
financial factors in stock selection and portfolio construction. Also, the portfolio measurement
has also been expanded to factors-level except the return and position which is insufficient for
actionable insights and understanding of market trends. We introduce the progress to date by
summarizing the methods we have developed that address the aforementioned research problems.
Thus we present sPortfolio, which is the first visual analytic system that attempts to
explore the factor investment area. In particular, sPortfolio provides a holistic overview of the
factor data and aims to facilitate quantitative market analysis. We also design 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. In the last, we
briefly discuss future research works as well as open questions in financial visualization area.
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