Financial Intelligence and Strategy Extraction via Visual Analysis Approaches

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Financial Intelligence and Strategy Extraction via Visual Analysis 
Approaches"

By

Mr. Xuanwu YUE


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, etc. 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 interactive 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 we addressed 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 Bitcoin 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 visualization 
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 visualization 
for financial data.


Date:			Monday, 16 December 2019

Time:			2:00pm - 4:00pm

Venue:			Room 2132C
 			Lift 19

Chairman:		Prof. Volkan KURSUN (ECE)

Committee Members:	Prof. Huamin QU (Supervisor)
 			Prof. Dimitris PAPADOPOULOS
 			Prof. Nevin ZHANG
 			Prof. Xuhu WAN (ISOM)
 			Prof. Xiaoru YUAN (Peking University)


**** ALL are Welcome ****