Matching Items (60)
Description
During the past decade, the Chinese bond market has been rapidly developing. The percentage of bond to total social funding is constantly increasing. The structure and behavior of investors are crucial to the construction of China’s bond market. Due to specific credit risks, bond market regulation usually involves in rules to control investor adequancy. It is heatedly discussed among academia and regulators about whether individual investors are adequate to directly participate in bond trading. This paper focuses on the comparison between individual and institutional bond investors, especially their returns and risks. Based on the comparison, this paper provides constructive suggestions for China’s bond market development and the bond market investor structure.
ContributorsLiu, Shaotong (Author) / Gu, Bin (Thesis advisor) / Zhu, Ning (Thesis advisor) / Yan, Hong (Committee member) / Arizona State University (Publisher)
Created2016
Description
Given the "New Nine Measures" for capital market reform, a policy document issued by the State Council of China, the development of markets for interest rate derivatives, such as treasury futures, becomes an increasingly important task. Several shortcomings of the existing treasury futures market have been noted: including low market liquidity, singular investor composition, restrict contract terms, and low hedging demand.
This study contributes to a better understanding of the treasury futures market by analyzing changes in China treasury futures market regulations and their impact on market liquidity of treasury futures. Found that compared with the mature market, China treasury futures market exists liquidity shortage, the trading system, market structure and the division of regulatory are factors which influence the liquidity of China treasury futures market.
This study found that reducing transaction costs for further optimization of the width and depth of China treasury futures market are not obvious by using quantitative analysis method, expanding the smallest change price can optimize the market depth, reducing transaction costs and expanding smallest change price can optimize the immediacy, volume and hosting amount. In addition, the bond market will also influence the treasury futures market, the price fluctuations and the morphology of the yield curve of bond market have significant influence on width, depth and holdings of market.
The system of China treasury futures market needs to be optimized by expanding the smallest change price and reducing transaction costs. The market structure needs to be optimized by establishing unified bond market and enriching investor structure.
These findings have significant theoretical and practical implications. The study also provides policy recommendations for the design and establishment of treasury futures market to the regulatory agencies.
This study contributes to a better understanding of the treasury futures market by analyzing changes in China treasury futures market regulations and their impact on market liquidity of treasury futures. Found that compared with the mature market, China treasury futures market exists liquidity shortage, the trading system, market structure and the division of regulatory are factors which influence the liquidity of China treasury futures market.
This study found that reducing transaction costs for further optimization of the width and depth of China treasury futures market are not obvious by using quantitative analysis method, expanding the smallest change price can optimize the market depth, reducing transaction costs and expanding smallest change price can optimize the immediacy, volume and hosting amount. In addition, the bond market will also influence the treasury futures market, the price fluctuations and the morphology of the yield curve of bond market have significant influence on width, depth and holdings of market.
The system of China treasury futures market needs to be optimized by expanding the smallest change price and reducing transaction costs. The market structure needs to be optimized by establishing unified bond market and enriching investor structure.
These findings have significant theoretical and practical implications. The study also provides policy recommendations for the design and establishment of treasury futures market to the regulatory agencies.
ContributorsMa, Jun (Author) / Gu, Bin (Thesis advisor) / Chen, Hong (Thesis advisor) / Wang, Tan (Committee member) / Arizona State University (Publisher)
Created2016
Description
The recent changes in the software markets gave users an unprecedented number
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.
ContributorsKanat, Irfan (Author) / Santanam, Raghu (Thesis advisor) / Vinze, Ajay (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2016
Description
By collecting and analyzing more than two million tweets, U.S. House Representatives’ voting records in 111th and 113th Congress, and data from other resources I study several aspects of adoption and use of Twitter by Representatives. In the first chapter, I study the overall impact of Twitter use by Representatives on their political orientation and their political alignment with their constituents. The findings show that Representatives who adopted Twitter moved closer to their constituents in terms of political orientation.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
ContributorsMosuavi, Seyedreza (Author) / Gu, Bin (Thesis advisor) / Vinzé, Ajay S. (Committee member) / Shi, Zhan (Michael) (Committee member) / Arizona State University (Publisher)
Created2016
Description
With the fast development of Chinese capital market, an increasing number of institutions and retail investors invest through professional managers. The key to evaluating investment manager’s skill and performance persistence largely lies in portfolio style research and attribution analysis.
The current dissertation takes advantage of a unique dataset, uncover hidden investment style and trading behavior, understanding their source of excess returns, and establishing a more comprehensive methodology for evaluating portfolio performance and manager skills.
The dissertation focuses on quantitative analysis. Highlights three most important aspects. Investment style determines the systematic returns and risks of any portfolio, and can be assessed ex-ante; Transaction can be observed and modified during the investment process; and return attribution can be implemented to evaluate portfolio (managers), ex-post. Hence, these three elements make up a comprehensive and logical investment process.
Investment style is probably the most important factor in determining portfolio returns. However, Chinese investment managers are under constant pressure to follow the market trend and shift style accordingly. Therefore, accurately identifying and predicting each manager’s investment style proves critically valuable.
In addition, transaction data probably provides the most reliable source of information in observing and evaluating an investment manager’s style and strategy, in the middle of the investment process.
Despite the efficacy of traditional return attribution methodology, there are clear limitations. The current study proposes a novel return attribution methodology, by synthesizing major portfolio strategy components, such as risk exposure adjustment, sector rotation, stock selection, altogether. Our novel methodology reveals that investment managers do not obtain much abnormal returns through risk exposure adjustment or sector rotation. Instead, Chinese investment managers seem to enjoy most of their excess returns through stock selection.
In addition, we find several interesting patterns in Chinese A-share market: 1). There is a negative relationship between asset under management (AUM) and investment performance, beyond certain AUM threshold; 2). There are limited benefits from style switching in the long run; 3). Many investment managers use CSI 300 component stocks as portfolio ballast and speculate with CSI500 and Medium-and-Small board component stocks for excess returns; 4). There is no systematic negative relationship between portfolio turnover and investment performance; despite negative relationship within certain sub-samples and sectors; 5). It is plausible to construct out-performing portfolios with style index funds and ETFs.
The current dissertation takes advantage of a unique dataset, uncover hidden investment style and trading behavior, understanding their source of excess returns, and establishing a more comprehensive methodology for evaluating portfolio performance and manager skills.
The dissertation focuses on quantitative analysis. Highlights three most important aspects. Investment style determines the systematic returns and risks of any portfolio, and can be assessed ex-ante; Transaction can be observed and modified during the investment process; and return attribution can be implemented to evaluate portfolio (managers), ex-post. Hence, these three elements make up a comprehensive and logical investment process.
Investment style is probably the most important factor in determining portfolio returns. However, Chinese investment managers are under constant pressure to follow the market trend and shift style accordingly. Therefore, accurately identifying and predicting each manager’s investment style proves critically valuable.
In addition, transaction data probably provides the most reliable source of information in observing and evaluating an investment manager’s style and strategy, in the middle of the investment process.
Despite the efficacy of traditional return attribution methodology, there are clear limitations. The current study proposes a novel return attribution methodology, by synthesizing major portfolio strategy components, such as risk exposure adjustment, sector rotation, stock selection, altogether. Our novel methodology reveals that investment managers do not obtain much abnormal returns through risk exposure adjustment or sector rotation. Instead, Chinese investment managers seem to enjoy most of their excess returns through stock selection.
In addition, we find several interesting patterns in Chinese A-share market: 1). There is a negative relationship between asset under management (AUM) and investment performance, beyond certain AUM threshold; 2). There are limited benefits from style switching in the long run; 3). Many investment managers use CSI 300 component stocks as portfolio ballast and speculate with CSI500 and Medium-and-Small board component stocks for excess returns; 4). There is no systematic negative relationship between portfolio turnover and investment performance; despite negative relationship within certain sub-samples and sectors; 5). It is plausible to construct out-performing portfolios with style index funds and ETFs.
ContributorsZhan, Yuyin (Author) / Gu, Bin (Thesis advisor) / Wang, Jiang (Thesis advisor) / Wang, Tan (Committee member) / Arizona State University (Publisher)
Created2016
Description
In the last two years, China’s booming of Internet Finance Platform made significant impacts on three dimensions. Compared with the conventional market, Internet Finance is asserted to open a revolutionary pathway of lending where by small and mid-sized companies may overcome the financing dilemma on credit accessibility and high cost. In other words, Internet Finance is hyped to be able to reduce information asymmetry, enhance allocation efficiency of resources, and promote product and process innovations for the financial institutions. However, the core essence of Internet Finance rests on risk assessment and control – a fundamental element applies to all forms of financing. Most current practice of internet finance on risk assessment and control remains unchanged from the mindset of traditional banking practices for small and medium sized firms. Hence, the same problems persisted and may only become even worse under the internet finance platform if no innovations take place.
In this thesis, the author proposed and tested a credit risk assessment model using data analytics techniques through an in-depth cases study with actual transaction data. Specifically, based on the 30,000 observations collected from actual transactional data from small and medium size firms of China’s home furnishing industry. The preliminary results are promising in spite of the limitations. The thesis concludes with the findings of relevance to improve the current practices and suggests areas of future research.
In this thesis, the author proposed and tested a credit risk assessment model using data analytics techniques through an in-depth cases study with actual transaction data. Specifically, based on the 30,000 observations collected from actual transactional data from small and medium size firms of China’s home furnishing industry. The preliminary results are promising in spite of the limitations. The thesis concludes with the findings of relevance to improve the current practices and suggests areas of future research.
ContributorsZhang, Qi (Author) / Pei, Ker-Wei (Thesis advisor) / Gu, Bin (Thesis advisor) / Cui, Haitao (Committee member) / Arizona State University (Publisher)
Created2016
Description
The city of Shanghai is set to become an international financial center (IFC) by 2020. To achieve this goal, it is imperative to clearly define the key characteristics of an IFC. In this study I draw from recent research on the ranking of IFCs to develop an index of these key characteristics that can be used to assess a city’s standings as an IFC. Based on a review of prior research, I first put together a comprehensive list of the indicators that have been used to evaluate IFCs, which includes six first-level indicators and 34 second-level indicators. I then collect information on all these indicators from public sources for the following eight cities each year from 2011 to 2013: London, New York, Paris, Hong Kong, Tokyo, Singapore, Beijing and Shanghai. Next, I conduct a principal component analysis (PCA) on my data, and obtain four primary factors that contain most information of the original 34 indicators. The first factor covers 18 of the original indicators and reflects a city’s level of development in general business environment. The second factor covers 10 of the original indicators and reflects a city’s level of development in financial markets. The third factor covers three of the original indicators and reflects a city’s level of economic vitality. The fourth factor covers three of the original indicators and reflects a city’s level of the costs of living. I further calculate the composite scores for the above eight cities along these four factors, and find that these eight cities can be classified into three tiers on the basis of their scores. The first tier consists of New York and London; the second tier consists of Singapore, Hong Kong, Paris and Tokyo; and the third tier consists of Shanghai and Beijing. I also find that Shanghai has been making progress in its scores along these four factors over the last three years, especially regarding financial market development, economic vitality, and cost of living. What Shanghai needs to focus on next is to improve its business environment so that it can move up to the second tier in IFC status.
ContributorsWang, Weiren, Ph.D (Author) / Yao, David (Thesis advisor) / Gu, Bin (Thesis advisor) / Chen, Hong (Committee member) / Arizona State University (Publisher)
Created2015
Description
Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model’s possible applications in practice as well as its implications for future research.
ContributorsZhou, Ye (Author) / Chen, Hong (Thesis advisor) / Gu, Bin (Thesis advisor) / Chao, Xiuli (Committee member) / Arizona State University (Publisher)
Created2015
Description
Mobile applications (Apps) markets with App stores have introduced a new approach to define and sell software applications with access to a large body of heterogeneous consumer population. Several distinctive features of mobile App store markets including – (a) highly heterogeneous consumer preferences and values, (b) high consumer cognitive burden of searching a large selection of similar Apps, and (c) continuously updateable product features and price – present a unique opportunity for IS researchers to investigate theoretically motivated research questions in this area. The aim of this dissertation research is to investigate the key determinants of mobile Apps success in App store markets. The dissertation is organized into three distinct and related studies. First, using the key tenets of product portfolio management theory and theory of economies of scope, this study empirically investigates how sellers’ App portfolio strategies are associated with sales performance over time. Second, the sale performance impacts of App product cues, generated from App product descriptions and offered from market formats, are examined using the theories of market signaling and cue utilization. Third, the role of App updates in stimulating consumer demands in the presence of strong ranking effects is appraised. The findings of this dissertation work highlight the impacts of sellers’ App assortment, strategic product description formulation, and long-term App management with price/feature updates on success in App market. The dissertation studies make key contributions to the IS literature by highlighting three key managerially and theoretically important findings related to mobile Apps: (1) diversification across selling categories is a key driver of high survival probability in the top charts, (2) product cues strategically presented in the descriptions have complementary relationships with market cues in influencing App sales, and (3) continuous quality improvements have long-term effects on App success in the presence of strong ranking effects.
ContributorsLee, Gun Woong (Author) / Santanam, Raghu (Thesis advisor) / Gu, Bin (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this study I investigate the factors that may influence consumer preference and choice in China’s home interior decoration industry. With the fast development of information technology such as the internet in China, it becomes increasingly important to have a more precise understanding of consumer preference and choice in home interior decoration decisions so that companies in this industry can provide better services to meet customer needs. Using survey data from a sample of potential customers and a sample of existing customers of a large home interior decoration company, I find that (1) internet has become the mostly used channel by consumers to gather information about home interior decoration, (2) design style is the most influential factor in consumers’ choice of home interior decoration company, and (3) consumers are more likely to choose home interior decoration companies to provide full services when they are between 35 to 45 years old or above 55 years old, when it is the first time for them to purchase a real estate property, and when they are located in the Eastern region of China. Findings of this study can help home interior decoration companies better understand customer needs and preferences, facilitate changes in their marketing and sales strategies, and consequently strengthen their competitive advantage．
ContributorsYang, Jin (Author) / Shen, Wei (Thesis advisor) / Zhang, Anmin (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2015