No description available.

No description available.

No description available.

No description available.

A significant portion of firms' capital expenditures in the recent past have been accomplished through the acquisition of the assets of other firms by tender offers. While the majority of tender offers are successful, a significant number of tender offers fail for various reasons. The accurate prediction of the success or failure of a tender offer would provide investors with valuable information upon which to base their investment decisions. The target firm shareholders could use the information as a base for their tendering decision. For the abitrageur, a predictive model would reduce the uncertainty associated with their investment activity and, thereby, increase their expected return. This study derives and tests an efficient market theory-based hypothesis for the prediction of inter-firm cash tender offer outcomes. This new hypothesis incorporates salient features from the Efficient Markets Theory, Contingent Claims Theory/Option Price Modeling, Capital Asset Pricing Theory and Event Study Methodology. The tender offer outcome prediction model is tested against a broadbased sample of tender offers to determine the formulation's accuracy and robustness. The study results are assessed in terms of how well the model predicts cash tender outcomes and whether or not market participants are rational and efficient in assessing the wealth-related consequences of investing in the securities of firms which are the target of tender offers.

No description available.

No description available.

No description available.

My dissertation consists of three chapters that study various aspects of stock return predictability. In the first chapter, I explore the interplay between the aggregation of information about stock returns and p-hacking. P-hacking refers to the practice of trying out various variables and model specifications until the result appears to be statistically significant, that is, the p-value of the test statistic is below a particular threshold. The standard information aggregation techniques exacerbate p-hacking by increasing the probability of the type I error. I propose an aggregation technique, which is a simple modification of 3PRF/PLS, that has an opposite property: the predictability tests applied to the combined predictor become more conservative in the presence of p-hacking. I quantify the advantages of my approach relative to the standard information aggregation techniques by using simulations. As an illustration, I apply the modified 3PRF/PLS to three sets of return predictors proposed in the literature and find that the forecasting ability of combined predictors in two cases cannot be explained by p-hacking. In the second chapter, I explore whether the stochastic discount factors (SDFs) of five characteristic-based asset pricing models can be explained by a large set of macroeconomic shocks. Characteristic-based factor models are linear models whose risk factors are returns on trading strategies based on firm characteristics. Such models are very popular in finance because of their superior ability to explain the cross-section of expected stock returns, but they are also criticized for their lack of interpretability. Each characteristic-based factor model is uniquely characterized by its SDF. To approximate the SDFs by a comprehensive set of 131 macroeconomic shocks without overfitting, I employ the elastic net regression, which is a machine learning technique. I find that the best combination of macroeconomic shocks can explain only a relatively small part of the variation in the SDFs, and the whole set of macroeconomic shocks approximates the SDFs not better than only few shocks. My findings suggest that behavioral factors and sentiment are important determinants of asset prices. The third chapter investigates whether investors efficiently aggregate analysts' earnings forecasts and whether combinations of the forecasts can predict announcement returns. The traditional consensus forecast of earnings used by academics and practitioners is the simple average of all analysts' earnings forecasts (Naive Consensus). However, this measure ignores that there exists a cross-sectional variation in analysts' forecast accuracy and persistence in such accuracy. I propose a consensus that is an accuracy-weighted average of all analysts' earnings forecasts (Smart Consensus). I find that Smart Consensus is a more accurate predictor of firms' earnings per share (EPS) than Naive Consensus. If investors weight forecasts efficiently according to the analysts' forecast accuracy, the market reaction to earnings announcements should be positively related to the difference between firms' reported earnings and Smart Consensus (Smart Surprise) and should be unrelated to the difference between firms' reported earnings and Naive Consensus (Naive Surprise). However, I find that market reaction to earnings announcements is positively related to both measures. Thus, investors do not aggregate forecasts efficiently. In addition, I find that the market reaction to Smart Surprise is stronger in stocks with higher institutional ownership. A trading strategy based on Expectation Gap, which is the difference between Smart and Naive Consensuses, generates positive risk-adjusted returns in the three-day window around earnings announcements. / Business Administration/Finance

This dissertation examines various aspects of human capital and their linkage to the financial markets. The first chapter empirically shows that the cost of debt is systematically higher for firms that operate in mobile labor markets. We posit two channels through which labor mobility could positively affect firms’ cost of debt. First, relates to greater default risk arising from potential loss of key personnel and a corresponding reduction in future cash flows, while the second relates to lower liquidation value (collateral) given that the firms’ human capital is more transient, which reduces pledgeable assets. Using across state, cross-sectional variations in the degree of enforceability of non-compete agreements which restrict employee mobility as a proxy for anticipated labor mobility, and state-level reforms to non-compete laws to capture exogenous shocks to labor mobility, we find that labor mobility (inverse of the strength of non-compete enforceability) has a significantly positive effect on the credit spreads of public corporate bonds (our measure of the cost of debt) issued from 1990 – 2014 for large, U.S. industrial firms. Moreover, the analysis reveals that the effect of labor mobility is greater for firms that are located in states which have a higher concentration of industry rivals or for firms that are comprised primarily of professional, knowledge workers, which corroborates the main results. Overall, these findings suggest that creditors price financial contracts by taking into account the risk that arises from labor mobility. The second chapter examines the effect of shareholder monitoring on the relation between human capital and firm value. The extant literature suggests that influential, concentrated ownership facilitates close shareholder monitoring and reduces information asymmetries between shareholders and the firm (Demsetz, 1985; Anderson and Reeb, 2003). Yet, intense monitoring by shareholders can impede employees’ initiatives and effort (Shleifer and Vishny, 1988; Burkart, Gromb, and Panunzi, 1997). We argue that such a cost can be significant when firm output relies on specialized – rather than more generic – human capital, which require self-motivation and autonomy to be productive. Consistent with our argument, the empirical evidence indicates that firm value suffers in the presence of highly influential ownership, but only when firm productivity depends on specialized human capital. We do not find such an effect when human capital is more generalized. Specifically, we observe that an equity portfolio that is long on firms with influential ownership and short on firms without influential ownership earns a significantly negative abnormal return from 2002 to 2010, but again, only for firms with specialized human capital. Overall, our results delineate the importance of considering the linkages between human capital and financial markets, which could impact the allocation of capital in the economy, and moreover, on economic growth. / Business Administration/Finance