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This paper investigates the performance of private equity partnerships using a data set of individual fund returns collected by Venture Economics. Over the sample period, average fund returns net of fees approximately equal the S&P 500 although there is a large degree of heterogeneity among fund returns. Returns persist strongly across funds raised by individual private equity partnerships. The returns also improve with partnership experience. Better performing funds are more likely to raise follow-on funds and raise larger funds than funds that perform poorly. This relationship is concave so that top performing funds do not grow proportionally as much as the average fund in the market. At the industry level, we show that market entry in the private equity industry is cyclical. Funds (and partnerships) started in boom times are less likely to raise follow-on funds, suggesting that these funds subsequently perform worse. Aggregate industry returns are lower following a boom, but most of this effect is driven by the poor performance of new entrants, while the returns of established funds are much less affected by these industry cycles. Several of these results differ markedly from those for mutual funds.

The decisions of the federal government such as new legislation, bills and reforms can have serious implications for the financial markets, especially in terms of corporate profitability and shareholder value. As the overseers of federal agencies, U.S. Senators are arguably the most important participants in these decision making processes, in addition to being the most informed investors in the market. As such, Senators may be able to capitalize on their superior networks and information through stock trading. The portfolios traded on such insider information should generate abnormal returns against the market index. This study conducts an analysis of the abnormal returns on common stock purchases reported by U.S. Senators between 1995 and 2012. This paper recreates congressional buy portfolio using a consistent methodology throughout the entire sample period considered by earlier studies. While the sample size is reduced, the methodology used in this analysis relies on actual transactions dates to maximize accuracy. An analysis of the abnormal returns of the common stock investments of U.S. Senators during the period 1995 – 2012 shows that Senators are generally poor investors: purchases made by senators underperform the market index by approximately 3% a year.

Spearman’s law of diminishing returns (SLODR) posits that at higher levels of general cognitive ability, the general factor (g) performs less well in explaining individual differences in cognitive test performance. The present study used factor mixture modeling to investigate SLODR in the Kaufman Assessment Battery for Children--Second Edition (KABC-II). Factor mixture modeling was a useful method to study SLODR because group membership was determined based on probabilities derived from the model. A second-order confirmatory factor model, consistent with three-stratum theory (Carroll, 1993), was modeled as a within-class factor structure. The fit of several models with varying number of classes and factorial invariance restrictions were compared. A sex covariate was also included with the models that provided the best fit for the data. The results indicated that a two-class model, which allowed for g mean differences, and class-specific g variances and subtest residual variances, provided the most parsimonious explanation of the data. Consistent with SLODR, the second-order general factor explained less subtest variance and less variance in the first-order factors for those of higher general ability. The standardized subtest residual variances were also larger in the high ability class than in the low ability class. Controlling for g, boys performed higher than girls in visual-spatial ability in each of the low and high ability classes. The findings from this study have implications for future research on the interpretation of intelligence test scores across the ability distribution. / text

Under mild assumptions, the expected returns of call options increase as the strike price becomes higher. Two ways to define option moneyness are the ratio of strike price to stock price (K/S ratio) and log(K/S)/σ. This paper examines the positive relationship between the call option returns and the correspondent risks by establishing linear models regarding the option returns and the two ratios. Furthermore, these ratios can be used to predict the option returns based on the regression models in practice. / text

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 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.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Virginia Tech contributes significantly to social and economic progress at the regional, state and national levels through its teaching, research and public service activities. The study is motivated by the current debate on the appropriate level of support for higher education in Virginia's largest university. This study measures the benefits of the university's undergraduate teaching mission. The results suggest that a VT undergraduate degree significantly increases the lifetime earnings of the graduates and also leads to several other benefits to the graduate, to his/her family, and to society. This study can help policy-makers to better understand the important contribution of Virginia Tech's teaching mission to society, and thus make more-informed decisions regarding the appropriate level of support for higher education. / Master of Science

In this dissertation, I explore the impact of idiosyncratic risk on asset returns. The first essay examines how idiosyncratic risk affects the cross-section of stock returns. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size effect, value premium, return momentum and short-term reversal to measure relative mispricing. I find that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or firm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these findings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost. The second essay studies the cross-sectional determinants of delta-hedged stock option returns with an emphasis on the pricing of volatility risk. We find that the average delta-hedged option returns are significantly negative for most stocks, and they decrease monotonically with both total and idiosyncratic volatility of the underlying stock. Our results are robust and cannot be explained by the Fama-French factors, market volatility risk, jump risk, or the effect of past stock return and volatility-related option mispricing. Our results strongly support a negative market price of volatility risk specification that is proportional to the volatility level. Reflecting this volatility risk premium, writing covered calls on high volatility stocks on average earns about 2% more per month than selling covered calls on low volatility stocks. This spread is higher when it is more difficult to arbitrage between stock and option. / text