The role of liquidity in asset pricing model has attracted much attention in recent financial studies; however there is a paucity of literature with respect to liquidity risk and asset pricing in the direct housing market. The housing market is characterized by costly searching, inelastic supply and short-sale constraints. It is expected that the housing market should incur more significant illiquid effects, since it is much more illiquid than stock market. Motivated by this intuition, this thesis aims to explore 1) whether and to what extent liquidity can explain variations in over-time/crosssectional housing returns; and 2) whether liquidity factor plays a role in explaining the second moment (i.e. volatility) of housing price. We employ the panel regression and Fama-MacBeth two-stage procedure to investigate over-time and cross-sectional relations between liquidity and housing return. Liquidity asset pricing theory suggests that assets with lower degree of liquidity offer higher expected returns. Consistent with this prediction, the panel regression results suggest that housing return is a decreasing function of liquidity in previous year, while it is positively relative to contemporary liquidity shocks. For the cross-sectional asset pricing tests, housing estate specific betas are estimated using rolling time-series regressions of a three-factor asset pricing model. We investigate the proposition that housing estates with greater return sensitivity to market liquidity earns higher expected return. Using a disaggregate dataset of 55 popular housing estates, we find (1) both market liquidity beta and housing estate specific liquidity risk are significantly priced in the cross-sectional housing estate returns, implying that cross-sectional differences in estate premium partially represent the liquidity premium. (2) The market beta, sentiment beta and market liquidity beta explain 14.36% of variations in cross-sectional estate returns. The results are robust across different specifications. (3) Investors are less willing to bear liquidity risk during the down markets, which shed new light on the positive price-volume correlation. These findings complement the cross-sectional liquidity-return relationship in the financial literature. Measuring housing price volatility is fundamental to the study of the dynamics of housing price risk. We investigate the effects of liquidity on housing price volatility in different housing classes (classified by size of the housing unit according to the Rating and Valuation Department’s definitions). Housing price volatility is measured as the conditional variance of a GARCH model under the Adaptive Expectations framework. We reveal that volatility transmits from small housing units to large housing units, which indirectly supports the trade-up effect in previous literature. Besides, the starter and high-end housing classes are extraordinarily sensitive to negative and positive liquidity shocks respectively. Consistent with the friction search theory, we find that the pricing errors are alleviated as the trading volume increases, since the valuated price tends to be more accurate as more information arrives. Lastly, the variance decomposition and impulse response results imply that the positive liquidity shock accounts for a large proportion of variations in housing volatility. / published_or_final_version / Real Estate and Construction / Doctoral / Doctor of Philosophy

My dissertation finds that the effects of institutional trading on stock price efficiency are significant and complicated. On one hand, I present evidence that institutional trading in general improves price efficiency. In particular, major stock market anomalies such as stock return momentum, post earnings announcement drift, and the book-to-market effect are much stronger in stocks with lower institutional trading volume. On the other, some institutional trading behaviors could hamper stock price efficiency even though institutions are generally rational arbitrageurs. Specifically, I show that when institutions act as positive-feedback traders, their trading contributes to stock return momentum and hampers prices efficiency.

published_or_final_version / Business / Master / Master of Philosophy

Numerous studies of the behavior of speculative prices have shown that the empirical distribution of such returns is consistently more peaked and fat-tailed than a Gaussian, and often positively skewed. Strong evidence is presented indicating hat such returns are better modeled by two-and three-component normal mixtures. By varying the means, variances, and probability weights of the component normals, a wide variety of peaked, fat-tailed, and symmetric or skewed distributions may be represented with very tractable mathematical expressions. Examination of the returns of 116 CBOE firms over three two-year periods indicates a high percentage of good fits for such normal mixtures, based on the chi-square statistic. Further, inspection of the parameters estimated for the two-component normal mixture reveals that the larger variance is quite frequently not associated with the lower probability weight as hypothesized by Mandelbrot and others. A new method of selecting class-boundaries is proposed to improve the reliability of the chi-square goodness-of-fit test. Using simulation, this method is found to be superior to the traditional Mann-Wald equiprobable approach, particularly for low priced securities. Using the assumption of risk-neutrality and a mixture of normals density for the underlying security returns, the mixture call option pricing model is derived. Call option prices are shown to be weighted sums of Black-Scholes prices, with solutions to the mixture model converging to Black-Scholes prices, with solutions to the mixture model converging to Black-Scholes solutions as the number of periods to expiration becomes large. Using the parameters obtained from typical mixture densities of actual CBOE firms, mixture model prices are generated and compared with Black-Scholes prices. It is found that out of the money, near term options are underpriced by Black-Scholes relative to the mixture model. The closer to expiration and the farther out of the money the option, the more Black-Scholes under-prices relative to the mixture model. Additionally, the fatter tailed and more positively skewed the underlying security returns distribution, the greater the differences between the two call option pricing models.

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Although computers have long been studied in terms of their changing price/performance ratio, the issue of accounting for performance in computer systems has not been adequately addressed. This paper addresses the topic in three ways. First, a survey of IS Managers and business "power-users" of personal computers was conducted to empirically determine the attributes of computer systems that provide value to users; these results guide subsequent choices regarding the operationalisation of user value. Second, an index of system performance was developed from published performance benchmarks and used as a direct measure of performance in the hedonic function. Third, a set of technical proxies was shown to adequately reproduce the performance index derived above, and was used in an alternate specification of the hedonic function. Using data on IBM-PC compatible laptop and desktop systems, price indexes were constructed using both approaches to performance measurement. The results demonstrated that both approaches yielded good explanatory power and nearly identical estimates of the rate of quality adjusted price change in PC systems. Thus, the set of technical proxies could be used to operationalise performance in a larger data set for which direct performance measures are unavailable. For the 1990s, laptop PCs were found to have decreased in quality adjusted price at an average of 39% per year while the corresponding figure for desktop PCs was 35% per year.

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