Thesis (Ph. D.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 151-159). Also available in print.

In Chapter 1, I investigate whether returns of strategies based on asset pricing anomalies exhibit time series persistence which can be attributed to flow-induced trading by mutual funds. I find persistence for thirteen characteristics, which is statistically significant for five including size, corporate investment, and bankruptcy likelihood. The persistence is not explained by individual stock momentum and is not limited to certain calendar months. The return predictability can be used to construct new trading strategies, which on average earn 4.5% annually. A price pressure measure of mutual fund flow-driven trading explains a substantial part of the strategy performance persistence. In Chapter 2, we propose a new approach for estimating expected returns on individual stocks from firm characteristics. We treat expected returns as latent variables and develop a procedure that filters them out using the characteristics as signals and imposing restrictions implied by a one factor asset pricing model. The estimates of expected returns obtained by applying our method to thirteen asset pricing anomalies generate a wide cross-sectional dispersion of realized returns. Our results provide evidence of strong commonality in the anomalies. The use of portfolios based on the filtered expectations as test assets increases the power of asset pricing tests. In Chapter 3, we examine the sensitivity of fourteen asset pricing anomalies to extreme observations using robust regression methods. We find that although all anomalies except size are strong and robust for stocks with presumably low returns, most of them are sensitive to individual influential observations for stocks with presumably high returns. For some anomalies, extreme observations distort regression results for all stocks and even portfolio returns. When the impact of such observations is mitigated, eight anomalies become positively related to expected returns for stocks with low characteristics meaning that these anomalies have an inverted J-shaped form. Chapter 4 concludes by summarizing the main contributions of three chapters and their implications. / text

This thesis consists of two essays analyzing the effect of uncertainty in macroeconomic and financial settings. Inspired by the counter-cyclical pattern of uncertainty and the role played by capital reallocation in Total Factor Productivity, we propose a theoretical viewpoint on uncertainty-driven business cycles in the first essay. Relying on the interaction between financial market and real sector, we are able to build up a transmission mechanism from uncertainty to business cycle by introducing a financial contract between firms and financial intermediaries. By setting up two types of firm with different production technology in a general equilibrium model, we show that information asymmetry leads firms with financing needs to be financially constrained. Due to information asymmetry, first best case is unachievable and production resources are allocated more to firms without financing needs. When uncertainty changes, the lending decision of financial intermediary also changes, further affecting firms’ production capacities. Production resources are reallocated between the two types of firms which generates fluctuations in TFP and other aggregates. More importantly, firms with financing needs is assumed with better production technology than the one adopted by the other type on average. Increase in uncertainty worsens the informational problem, reduces funds provided to firms with better technology, causes reallocation of resources to the other type, and further decreases productivity of the economy as a whole. This is in line with an economic downturn and also consistent with the counter-cyclicality of uncertainty. We also conduct a quantitative analysis by calibrating the model to the data and the estimated results provide corroborating evidence for the theory. Using a merged data-set of US firms during years 1971-2008, we empirically examine the impact of uncertainty on capital reallocation via financial friction in the second essay. By adopting KZ index as an indicator for firms’ financial statuses, we decompose the uncertainty-capital reallocation relation into three hypotheses. Using cross-sectional dispersion of stock return as a measure for uncertainty, we find that uncertainty is negatively associated with firms’ financial statuses. A firm with high uncertainty level is more likely to be in a low position of financial status. Second, uncertainty is in a negative relation with capital reallocation, which means capital reallocation decreases at firm level when uncertainty increases. Third, by sorting firms into different groups based on their financial statuses, we find that firms which are in worse financial situation are more responsive to uncertainty change. The finding is consistent with our prediction that uncertainty affects capital reallocation through financial friction. We employ both reduced-form and structural estimation strategies to examine our predictions, and all regression results are supportive. To further test the role of financial friction in the relation, we also sort firms into different groups by SIC code. And we find that, firms in industries relying more on financial market for external financing are more responsive to uncertainty change. / published_or_final_version / Economics and Finance / Doctoral / Doctor of Philosophy

This dissertation contains two essays that study the implications of information arrival on asset prices. In the first essay, I study an important aspect of the firm-level information structure - the quantity of information - and its effect on the cross-section of stock returns. The main contribution of this essay is to propose a new proxy for information intensity (monthly information quantity) and establish a link between information intensity and stock returns. I find that higher information intensity reduces expected uncertainty and leads to a lower expected return, after controlling for a variety of traditional risk factors and asset pricing anomalies. An information-intensity-based long-short portfolio generates an abnormal return of 4.44% per year. My findings suggest that, as a key component of information structure, information quantity is of first order importance in determining stock returns, and more generally, that investor learning plays an important role in financial markets with incomplete information. The second essay, based on a joint work with John Maheu and Tom McCurdy, studies the asset-pricing implication of market-level information arrival, which can lead to large movements (jumps) in the market index. Deviating from the literature that studies the impact of jumps through option pricing and motivated by a nonlinear pricing kernel associated with general preferences, we focus on the pricing impact of jumps through the pricing of higher-order moments. We find that three components of a modeling device, including: a 2-component GARCH model for diffusive volatility, an autoregressive model for jump intensity, and a higher order moment specification of the equity premium, are particularly important for asset pricing with jumps. This modeling device enables us to be the first to uncover significant pricing of both diffusive risk and jump risk, using only a time series of equity return data. We find that the risk premium due to jumps is a significant part of the overall equity premium. Our results also suggest the existence of a significant skewness premium and offer a potential resolution to sometimes conflicting results on the intertemporal risk-return relationship. Furthermore, taking jumps into account improves the out-of-sample performance of a portfolio allocation application.

This study analyzes the impact of information quality on option returns. I find that firms with low-quality information have call option returns that are significantly lower than firms with high information quality. The findings hold in- and out-of-sample, over different time periods, and are robust to a battery of asset pricing tests. The results suggest that the risk caused by poor information quality has a powerful and non-diversifiable impact on the expected returns of option contracts. Further analysis shows that these results are important when examining the cross-sectional link between stock returns and information quality. Firms with low information quality and equity that is "option-like" have significantly lower future stock returns than firms with high information quality. If this option-like effect is not controlled for in empirical tests, the association between information quality and the cross-section of stock returns is often flat. This is especially true for accounting-based proxies of information quality.

This study analyzes the impact of information quality on option returns. I find that firms with low-quality information have call option returns that are significantly lower than firms with high information quality. The findings hold in- and out-of-sample, over different time periods, and are robust to a battery of asset pricing tests. The results suggest that the risk caused by poor information quality has a powerful and non-diversifiable impact on the expected returns of option contracts. Further analysis shows that these results are important when examining the cross-sectional link between stock returns and information quality. Firms with low information quality and equity that is "option-like" have significantly lower future stock returns than firms with high information quality. If this option-like effect is not controlled for in empirical tests, the association between information quality and the cross-section of stock returns is often flat. This is especially true for accounting-based proxies of information quality.

This dissertation contains two essays that study the implications of information arrival on asset prices. In the first essay, I study an important aspect of the firm-level information structure - the quantity of information - and its effect on the cross-section of stock returns. The main contribution of this essay is to propose a new proxy for information intensity (monthly information quantity) and establish a link between information intensity and stock returns. I find that higher information intensity reduces expected uncertainty and leads to a lower expected return, after controlling for a variety of traditional risk factors and asset pricing anomalies. An information-intensity-based long-short portfolio generates an abnormal return of 4.44% per year. My findings suggest that, as a key component of information structure, information quantity is of first order importance in determining stock returns, and more generally, that investor learning plays an important role in financial markets with incomplete information. The second essay, based on a joint work with John Maheu and Tom McCurdy, studies the asset-pricing implication of market-level information arrival, which can lead to large movements (jumps) in the market index. Deviating from the literature that studies the impact of jumps through option pricing and motivated by a nonlinear pricing kernel associated with general preferences, we focus on the pricing impact of jumps through the pricing of higher-order moments. We find that three components of a modeling device, including: a 2-component GARCH model for diffusive volatility, an autoregressive model for jump intensity, and a higher order moment specification of the equity premium, are particularly important for asset pricing with jumps. This modeling device enables us to be the first to uncover significant pricing of both diffusive risk and jump risk, using only a time series of equity return data. We find that the risk premium due to jumps is a significant part of the overall equity premium. Our results also suggest the existence of a significant skewness premium and offer a potential resolution to sometimes conflicting results on the intertemporal risk-return relationship. Furthermore, taking jumps into account improves the out-of-sample performance of a portfolio allocation application.

This dissertation consists of two essays, in which I examine the effects of incomplete or noisy information on expected risk premium in equity markets. In the first essay I provide empirical evidence demonstrating that an information-quality (IQ) factor, built on accrual-based information precision measure, is priced. This result still stands after controlling for factors, such as size, Book-to-Market (B/M) ratio, and liquidity. To explain this empirical observation, I derive a continuous-time model in the spirit of Merton’s (1973) Intertemporal Capital Asset Pricing Model (ICAPM) to examine how systematic IQ risk affects security returns. Unique to my model, imprecise information influences the pricing of an asset through its covariance with: (i) stock return; (ii) market return; and (iii) market-wide IQ. In equilibrium, the aggregate effect of these covariance terms (proportional to IQ-related betas) represents the systematic component of IQ risk and therefore requires a risk premium to compensate for it. My empirical test confirms that the aggregate effect of systematic IQ risk is significant and robust to the inclusion of other risk sources, such as liquidity risk. In the second essay I extend a recent complete information stock valuation model with incomplete information environment. In practice, mean earnings-per-share growth rate (MEGR) is random and unobservable. Therefore, asset prices should reflect how investors learn about the unobserved state variable. In my model investors learn about MEGR in continuous time. Firm characteristics, such as stronger mean reversion and lower volatility of MEGR, make learning faster and easier. As a result, the magnitude of risk premium due to uncertainty about MEGR declines over learning horizon and converges to a long-term steady level. Due to the stochastic nature of the unobserved state variable, complete learning is impossible (except for cases with perfect correlation between earnings and MEGR). As a result, the risk premium is non-zero at all times reflecting a persistent uncertainty that investors hold in an incomplete information environment.

Previous work by researchers as Eugene F. Fama and Kenneth R. French, show that average return on stocks are related to a firms characteristics like size and book-to-market ratio. These kinds of patterns in average return is not explained by The Capital Asset Pricing Model (CAPM), and are therefore seen as anomalies. Fama and French have proposed a three-factor model, which captures patterns observed in U.S average returns associated with size and value. Since the previous research on this topic is limited in Sweden we find it interesting to study companies listed on the Swedish stock exchange “Nasdaq OMX Stockholm”. This study finds that the average return on Swedish stocks seems to be related to size and value. The two additional variables in the three-factor model help explain the variation on the Swedish stock market for the period 2011-2013.

The last decade has seen significant developments in highway asset management. A key component to successful asset management is long-term network investment planning. In order to successfully manage a significant quantity of aging roadway infrastructure and growing traffic volume, agencies are faced with challenges in developing reliable long term plans that maximize the network performance through value optimization. Current practice typically involves relatively independent planning for the bridge and pavement networks; with a very slight number of situations allowing for reliable trade-off analysis between the two. While a situation in which the choice to improve two structures rather than one pavement section may yield a greater percentage increase in the bridge network performance, than the opposite choice would for the pavement network - the reliability of this choice being right and at the right time significantly decreases over time. Introduction of mutually inclusive highway asset planning in this research, by integration of the bridges into an equivalent measure of the pavement network results in significant increases in the long-term planning reliability - is proposed. Data from the Ministry of Transportation of Ontario is used to demonstrate how this proposed approach would work. A key point of this Strategic Total Highway Asset Management Integration (STHAMi) approach is the Conceptual Structural Integration Factor (CSIF). Application of CSIF and Bridge Condition Index (BCI) integration into a pavement performance index allows for representation and treatment of bridges as equivalent pavement sections. This allows for a better comparison of the assets over time. Compared to the traditional approach of mutually exclusive network level planning, STHAMi resulted in a higher percentage of network treated per unit of value, coupled with consistently higher annual network performance over the long-term. In addition to significantly higher long-term sub-asset trade-off reliability, STHAMi offers potential for significant increases in organizational efficiency with respect to longterm highway asset planning. Key benefits include introduction of one pavement performance indicator as an all encompassing performance indicator for the complete highway asset, as well as the potential for long-term bridge network level planning execution within a pavement engineering oriented organizational unit. Further STHAMi development is recommended through integration of other network performance measures such as operational and safety indicators.