abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2016

China's rapid economic growth and the development of its domestic stock market have attracted considerable attention from foreign investors. China's economic financial expansion, however, has emerged from an environment of state planning and radical socialist ideology. With a view of providing investors with a better understanding of the risk and return relationship in the Chinese A-share market over the past decade, this thesis adapts several empirical models to the circumstances in China and conducts four empirical analyses. First, in order to rationalize foreign investors' entry into the A-share market, the thesis compares the diversification benefits in three China-related stock markets, namely the A-share, the B-share and the H-share markets in a mean-variance framework using daily, weekly and monthly data respectively. The results suggest that of the three stock markets, the B-share market generates the highest average annual returns while the A-share market has the most significant diversification benefits regardless of whether the analysis is undertaken implementing a traditional mean-variance framework or a downside risk framework. Next, an empirical analysis using the Fama and MacBeth two-pass procedure is undertaken to test the relationship between beta, firm factors and stock returns. Similar to the findings in other stock markets, the results of this analysis show that the static betas for individual stocks fail to capture variation in stock returns in the A-share market. In contrast, the effects of book-to-market and trading volume are significant in the sample period. However, the fact that none of these factors have a persistent role in explaining stock returns suggests a possible change in the investment philosophy of Chinese domestic investors over the past decade. In the third analysis, two global betas are incorporated into the cross-sectional regressions in a bid to examine the integration or segmentation of the A-share market with the world and Hong Kong stock markets. Specifically, both time-varying betas and static betas are used in the analysis. The results suggest that there is no beta effect and the A-share marke t is totally segmented from both the world and Hong Kong stock markets. Finally, when the segmentation and integration status of the A-share market is further examined using the Maximum Likelihood Estimation framework without beta estimation and the assumption of a linear relationship between beta and stock returns, the findings suggest that the A-share market is becoming increasing integrated with the B-share and the Hong Kong stock markets.

China's rapid economic growth and the development of its domestic stock market have attracted considerable attention from foreign investors. China's economic financial expansion, however, has emerged from an environment of state planning and radical socialist ideology. With a view of providing investors with a better understanding of the risk and return relationship in the Chinese A-share market over the past decade, this thesis adapts several empirical models to the circumstances in China and conducts four empirical analyses. First, in order to rationalize foreign investors' entry into the A-share market, the thesis compares the diversification benefits in three China-related stock markets, namely the A-share, the B-share and the H-share markets in a mean-variance framework using daily, weekly and monthly data respectively. The results suggest that of the three stock markets, the B-share market generates the highest average annual returns while the A-share market has the most significant diversification benefits regardless of whether the analysis is undertaken implementing a traditional mean-variance framework or a downside risk framework. Next, an empirical analysis using the Fama and MacBeth two-pass procedure is undertaken to test the relationship between beta, firm factors and stock returns. Similar to the findings in other stock markets, the results of this analysis show that the static betas for individual stocks fail to capture variation in stock returns in the A-share market. In contrast, the effects of book-to-market and trading volume are significant in the sample period. However, the fact that none of these factors have a persistent role in explaining stock returns suggests a possible change in the investment philosophy of Chinese domestic investors over the past decade. In the third analysis, two global betas are incorporated into the cross-sectional regressions in a bid to examine the integration or segmentation of the A-share market with the world and Hong Kong stock markets. Specifically, both time-varying betas and static betas are used in the analysis. The results suggest that there is no beta effect and the A-share marke t is totally segmented from both the world and Hong Kong stock markets. Finally, when the segmentation and integration status of the A-share market is further examined using the Maximum Likelihood Estimation framework without beta estimation and the assumption of a linear relationship between beta and stock returns, the findings suggest that the A-share market is becoming increasing integrated with the B-share and the Hong Kong stock markets.

This research attempts to test the performance of the Fama-French three-factor model (1993) in explaining the stock portfolio returns on the China A-share Stock Market from 1996 to 2005. We will follows Drew, Naughton and Veeraraghavan (2003) method, who adopted the Fama and French's (1993) method to test small sample stock markets. We find the positive relation between book-to-market ratio and stock excess returns, and the negative relationship between size and stock excess returns. And our result demonstrated that the three-factor model is more accurate in predicting stock excess returns than the CAPM, since the adjusted R² value increased and the intercept are not significantly different from zero. The size effect is stronger than the BTM ratio effect. Moreover, our results present that stock profitability is related to size and BTM ratio in China stock market. However, the relationship between stock profitability and size and BTM ratio are unconditional.

Notre thèse explore les facteurs de risque et les modèles des facteurs sur le marché boursier chinois A-share. Notre étude est basée sur le contexte du modèle facteur de Fama-French (FF). Tout d'abord, au chapitre 1, nous réexaminons l'applicabilité du Modèle Fama-French à Trois Facteurs (FF3F) et du dernier Modèle Fama-French à Cinq Facteurs (FF5F), compte tenu de plusieurs caractéristiques spéciales du marché boursier chinois. Les résultats empiriques montrent que le Modèle FF3F peut expliquer la majorité des variations de séries chronologiques des rentabilités des actions chinoises A-share. Au cours de la période d'échantillonnage, le marché bêta et le facteur SMB sont des déterminants importants pour expliquer la variation transversale des rentabilités des actions, cependant nous ne trouvons aucune prime de valeur. D’après la comparaison des performances des modèles FF3F et FF5F en présence de facteurs de rentabilité et d'investissement, le Modèle FF5F ne semble pas capturer plus de variations de rentabilités espérées que le modèle à trois facteurs, à l'exception des six portefeuilles pondérées en valeurs qui formés à partir de la taille et de la rentabilité opérationnelle.Dans le chapitre 2, nous examinons si les facteurs FF, SMB et HML, sont des proxys d'innovations de variables d'état sélectionnées (rendement de dividende agrégée, taux de T-bonds en un mois, l’écart de terme et l’écart de défaut) qui décrivent, sur la période recherche, les opportunités futures d'investissement sur le marché boursier chinois A-share. Les régressions chronologiques et les régressions des séries transversales sont réalisées sur cinq modèles comparatifs en utilisant l'approche à deux étapes Fama-MacBeth. Les facteurs FF ne perdent pas leur pouvoir explicatif, avec ou sans la présence des innovations des quatre variables d’états sélectionnées, à la fois dans les examens de séries chronologiques et les examens transversaux. Nous trouvons que l'information contenue dans l'innovation de rendements de dividende agrégés semble totalement capturée par la combinaison du marché bêta et du facteur de taille. Les facteurs FF ont pu jouer un rôle limité de capturer d'opportunités d'investissement alternatives représentées par les innovations des quatre variables d'état sélectionnées.Dans le chapitre 3, nous étudions si les facteurs FF sont des proxys de facteurs de risque de détresse et si différentes méthodes de construction des facteurs entraînent des résultats différents. Les résultats empiriques suggèrent qu'il n'y a pas de preuve significative que les facteurs FF représentent un risque de détresse sur le marché boursier chinois A-share. En comparant les résultats des régressions des séries chronologiques à partir de deux méthodes différentes, la performance du facteur de risque de détresse basé sur le DLI semble légèrement meilleure que celui basé sur le O-score. Cependant, le facteur de risque de détresse n'est pas un déterminant important des rentabilités transversales moyennes, et les facteurs FF ne peuvent pas représenter le facteur de risque de détresse dans la section transversale du marché boursier chinois A-share. / This dissertation is to explore the risk factors and factor models on Chinese A-share stock market based on the context of Fama-French (FF) factor model. First of all, chapter 1 re-examines the applicability of Fama-French Three-Factor (FF3F) Model and the latest Fama-French Five-Factor (FF5F) Model considering several special features of Chinese stock market. FF3F Model can explain a majority of time-series variation of the Chinese A-share stock returns. The market beta and SMB are important determinants in explaining the cross-sectional variation in the average stock returns over the sample period; however, we find no value premium. Comparing the performance of both FF3F Model and FF5F Model on Chinese A-share stock market, in the presence of profitability and investment factors, FF5F Model seems not capture more variations of expected stock returns than the three-factor model except the six value-weighted portfolios formed on size and operating profitability.Chapter 2 examines whether FF factors SMB and HML proxy for the innovations of selected state variables (aggregate dividend yield, one-month T-bill rate, term spread and default spread) that describe future investment opportunities on Chinese A-share stock market during the research period. Both time-series and cross-sectional regressions are performed on five comparative models using Fama-MacBeth two-stage approach. FF factors don’t lose their explanatory power with or without the presence of the innovations of selected four state variables in both the time-series and cross-sectional examinations. We find that the information contained in innovation of aggregate dividend yields seems totally captured by the combination of market beta and size factor. FF factors might have played a limited role in capturing alternative investment opportunities proxied by innovations of the selected four state variables.Chapter 3 investigates whether FF factors proxy for distress risk factor and whether different methods of constructing factors result in the different outcomes. The empirical results suggest that there is no significant evidence that FF factors are proxying for distress risk on Chinese A-share stock market. Comparing the time-series regression results by using two different methods, the distress risk factor constructed based on DLI seems to perform slightly better than that constructed based on O-score in capturing time-series average returns. However, the distress risk factor is not an important determinant of cross-sectional average returns, and FF factors cannot proxy as distress risk factor in the cross-section on Chinese A-share stock market.