A consensus exists in the body of academic literature that stocks with high BE/ME characteristics outperform stocks with low BE/ME characteristics. Researchers disagree, however, as to the cause of the phenomenon. Two competing theories have emerged. The value premium originates either from the relative riskiness of high BE/ME value and low BE/ME growth stocks or from the persistent irrational pricing of those stocks. Market participants question whether the long lineage of academic research showing the existence of the value premium can actually be applied to their portfolio decision-making. The lack of a pervasive value premium across stock size strata suggests the return phenomenon may result from information asymmetry or trading noise, and not from the pricing of greater risk. The value premium appears to be exclusively available to market participants who can effectively navigate the smallest, most illiquid segment of the stock market. In other words, the value premium does not appear to be available to large institutional investors.

Wang, Xiaolei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 34-39). / Abstracts in English and Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Identification of Stock Market Crashes --- p.5 / Chapter 2.1 --- Identification Criteria --- p.7 / Chapter 2.2 --- Identification Results --- p.8 / Chapter Chapter 3. --- Data --- p.10 / Chapter 3.1 --- Data Issue for Chapter 4 --- p.10 / Chapter 3.2 --- Data Issue for Chapter 5 --- p.12 / Chapter 3.3 --- Data Issue for Chapter 6 --- p.12 / Chapter Chapter 4. --- Examination of AFE --- p.13 / Chapter 4.1 --- Definition of AFE and MAAFE --- p.13 / Chapter 4.2 --- Examination of MAAFE --- p.14 / Chapter 4.3 --- Examination of AFE by Grouping Duration --- p.15 / Chapter Chapter 5. --- Examination of AFD --- p.18 / Chapter Chapter 6. --- Examination of the Relationship between AFD and ESR --- p.22 / Chapter 6.1 --- Portfolio Strategy - Sorting by Size and Dispersion --- p.23 / Chapter 6.2 --- Portfolio Strategy - Sorting by Size and Book to Market Ratio --- p.26 / Chapter 6.3 --- Fama-French Time Series Regression Test (Three-Factor Model) --- p.28 / Chapter 6.4 --- Fama-French Time Series Regression Test (Three-Factor Model with Dispersion on the Right Hand Side) --- p.30 / Chapter 6.5 --- Introduction of a Nonlinear Form of AFD to the Fama-French Model --- p.31 / Chapter Chapter 7. --- Conclusions --- p.32 / References --- p.34 / Appendix Table I to Table XVI --- p.40-55 / Figure I to Figure VI --- p.56-61

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This dissertation contains three chapters. The first chapter, Strategic Alliance and Endogenous Production Network, examines how U.S. firms’ involvement in strategic alliances interacts with their endogenous choice of production networks. The results reveal that the alliance firm is more likely to actively create and break supply chains, especially with customers or suppliers from the industries within the alliance-related industrial scope. Moreover, such interactions are stronger when the updated customers and suppliers have closer proximity to the alliance-related industries. To rationalize these stylized findings, we develop a model featured with the firm’s endogenous searching of supplier candidates and endogenous input sourcing strategy. Furthermore, strategic alliance is introduced as a mitigation of friction in candidate searching. The model implies that the strategic alliance could encourage the firm’s search for supplier candidates and boost the adding and dropping of production networks simultaneously. The second chapter, R&D, Risk Premia, and Credit Spreads, is motivated by the empirical evidence that among the U.S. firms with both publicly traded equity and bonds, the R&D-intensive ones tend to show higher expected equity returns, but lower leverage, default rates and credit spreads than R&D-non-intensive ones. To provide a unified explanation for these cross-sectional differences, we propose a production-based dynamic stochastic general equilibrium model featured with long-run risk and disaster risk. Specifically, we assume that the economy consists of an R&D and non-R&D sector. When involved in innovation, the R&D sector is assumed to face a rare disaster shock in the accumulation of the intangible R&D capital. The model implies that the high monopolistic rent increases the market value of R&D sector and generates a lower default rate and credit spread compared to the non-R&D sector. Besides, despite the low leverage capacity restricted by the non-collateralizable intangible capital, the business risk underlying the innovative activities plays a dominant role and results in an overall higher equity return of the R&D sector. Moreover, the model generates sizable heterogeneity in the quantities of interest between the R&D and non-R&D sector as observed from the data, and fits the aggregate macroeconomic and asset pricing moments reasonably well. The third chapter, Measuring Common and Industry-Specific Uncertainty: A Bayesian Approach, estimates the measures of the common and industry-specific uncertainty from U.S. quarterly industry-level financial characteristics by a Bayesian dynamic factor model. In this model, we assume that the industrial financial characteristics are driven by common and industry-specific factors that evolve by VAR processes, where the time-varying standard deviation of the corresponding innovation terms are considered as proxies of common and industrial uncertainty. Then, we compare the estimated common uncertainty measure with three existing aggregate uncertainty measures. The results suggest that our measure interacts with real economy and tracks the business cycles like the other three measures. Moreover, we test if these uncertainty measures could forecast the stock returns of industrial portfolios together with other moments estimated from the model. The results suggest that a time-varying linear forecasting model of the uncertainty measures performs well in return forecasting both in short and long run for most of the industries.

The literature investigating the relationship between stock market returns and inflation is long and has produced diverse findings. This thesis examines the nature of stock–inflation relations in Sub-Saharan countries whose stock markets were established before 1992. Evidence in this thesis shows that in the short term there is a positive relationship between stocks and inflation. Using the Johansen (1988) evidence, a long-run stock–inflation relationship is confirmed only in Nigeria and South Africa, where it is found to be negative. However, accounting for structural breaks provides evidence for a long-run relationship in Botswana, Ghana and Kenya. The evidence of the effects of regimes in the relationship is further supported by a nonparametric cointegration analysis which finds a long-run relation in countries where the Johansen (1988) method had failed. Unexpected inflation is also found to be related to stock returns in Botswana, Ghana, Kenya, Nigeria and Mauritius, which raises concerns about the use of month-end stock data in analysing this relationship. The thesis confirms the existence of hidden inflation in Kenya, Mauritius, Nigeria and Zimbabwe. Imported inflation, interest rates and the exchange rate are found to have useful information about inflation movements in Sub-Saharan Africa.

Wong Man Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 118-121). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Table of Contents --- p.v / List of Tables --- p.viii / List of Figures --- p.x / List of Appendices --- p.x / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Performance of Value Strategy in Stock Markets over The World --- p.7 / Chapter 2.2 --- Possible Explanations for Superior Return of Value Stocks --- p.11 / Chapter 2.2.1 --- Sampling Biases --- p.11 / Chapter 2.2.2 --- Risk Factors --- p.13 / Chapter 2.2.3 --- Expectation Error Hypothesis --- p.15 / Chapter 2.3 --- Studies for Value Strategy in Hong Kong --- p.20 / Chapter Chapter 3 --- Data and Methodology --- p.23 / Chapter 3.1 --- Methodology of Expectation Error Hypothesis --- p.23 / Chapter 3.1.1 --- Earnings Announcement Returns --- p.23 / Chapter 3.1.2 --- Past and Future Earnings Growth Rates of Stocks --- p.26 / Chapter 3.2 --- Data Source --- p.29 / Chapter 3.3 --- Portfolio Formation --- p.30 / Chapter 3.4 --- Variable Calculation Method --- p.31 / Chapter 3.4.1 --- Annual Buy and Hold Returns --- p.31 / Chapter 3.4.2 --- Earnings Announcement Returns --- p.32 / Chapter 3.4.3 --- Earnings Growth Rate of Portfolios --- p.33 / Chapter Chapter 4 --- Interpretation of Results --- p.34 / Chapter 4.1 --- Annual Buy and Hold Returns of Portfolios --- p.36 / Chapter 4.1.1 --- Annual Returns of Portfolios Sorted by B/M Ratio --- p.36 / Chapter 4.1.2 --- Annual Returns of Portfolios Sorted by E/P Ratio --- p.37 / Chapter 4.1.3 --- Analysis of Performance on Return Differences between Two Ratios --- p.38 / Chapter 4.2 --- Earnings Announcement Returns for Value and Glamour Portfolios --- p.41 / Chapter 4.2.1 --- 3-day Event Returns --- p.41 / Chapter 4.2.2 --- "B/M Ratio: 5,7,9 & 11 Days Event Returns" --- p.43 / Chapter 4.2.3 --- "E/P Ratio: 5,7,9 & 11 Days Event Returns" --- p.46 / Chapter 4.3 --- Past and Future Earnings Growths of Portfolios --- p.49 / Chapter 4.3.1 --- "Fundamental Variables, Prior and Post Returns of Portfolios" --- p.50 / Chapter 4.3.2 --- Earnings Performance of Portfolios --- p.51 / Chapter 4.3.3 --- Factors Affect Investor Expectation --- p.56 / Chapter Chapter 5 --- Conclusion --- p.59 / Tables --- p.64 / Figures --- p.76 / Appendices --- p.82 / References --- p.118