We examine the welfare effects of costly information acquistion in a version of the Grossman-Stiglitz (1980) exchange economy in which all traders are fully rational. We find, as emphasized by Hirschleifer, that information gathering leads the suboptimal risk sharing. Furthermore, information gathering worsens the equilibrium risk-return tradeoff faced by investors. We show that all investors would be better off if the information were not available and that the welfare costs of information gathering are greater when (i) the cost of acquiring information is lower; (ii) the prior return variance of the risky asset is higher; and (iii) the quality of the information is higher. We also show that the relative welfare costs to investors depends on the combination of their endowment and preferences.

This paper explains why seemingly irrational overconfident behavior can persist. Information aggregation is poor in groups in which most individuals herd. By ignoring the herd, the actions of overconfident individuals (“entrepreneurs”) convey their private information. However, entrepreneurs make mistakes and thus die more frequently. The socially optimal proportion of entrepreneurs trades off the positive information externality against high attrition rates of entrepreneurs, and depends on the size of the group, on the degree of overconfidence, and on the accuracy of individuals’ private information. The stationary distribution trades off the fitness of the group against the fitness of overconfident individuals.

The types of investments a firm undertakes will depend in part on what it expects the outcome of those investments to reveal about its skills, capabilities, and assets (i.e., its resources). We predict that a firm will specialize when young, then experiment in a new line of business for some time, and then either expand into a large, multisegment business or focus and scale up its specialized business. We derive several empirical implications for firm valuations and the reaction of stock prices to news about firm prospects. We also offer a novel explanation for the well-documented ‘‘diversification’’ discount.

We propose a formal definition of approximate arbitrage which can be used to extend the applicability of theories based on the absence of arbitrage. Our definition is based on the radio of gain to loss, where gain (loss) is the expectation of the positive (negative) part of the excess payoff. Arbitrage is characterized by infinite gain-loss ratio, an approximate arbitrage by gain-loss ratio close to infinity. Our definition of approximate arbitrage has a use dual interpretation in terms of pricing kernels. This allows us to compare the pricing kernel restriction implied by a limit on the maximum gain-loss ration to the other in the literature. We show theoretically that only the gain-loss restriction is consistent with the absence of arbitrage and approximate arbitrage opportunities. We demonstrate the practical differences of these alternative pricing kernel restrictions by examining their implications for the prices of call options on an asset that does not trade.

Our paper o�ers a minimalist model of a run on a financial market. The prime ingredient is that each risk-neutral investor fears having to liquidate after a run, but before prices can recover back to fundamental values. During the run, only the risk-averse market-making sector is willing to absorb shares. To avoid having to possibly liquidate shares at the marginal post-run price—in which case the market-making sector will already hold a lot of share inventory and thus be more reluctant to absorb additional shares—all investors may prefer selling their shares into the market today at the average run price, thereby causing the run itself. Consequently, stock prices are low and risk is allocated ineciently. Liquidity runs and crises are not caused by liquidity shocks per se, but by the fear of future liquidity shocks.

This paper examines how private information affects trading volume, the information content of trading volume data, and if there are any relations between trading volume and price changes which can be explained by informational differences. We develop a model with two trading periods in which asymmetrically informed agents learn the trading volume of the first round of trading prior to trading in the second round. We also develop a general method for computing equilibria in such a model assuming only smooth, concave utility functions, and asset return distributions and signals with smooth densities. The model prices (i) a positive relation between trading volume and the absolute value of price changes; (ii) a positive relation between trading volume and subsequent stock price volatility; and (iii) that positive price moments on high trading volume lead, on average, to positive price movements.

The first chapter of my dissertation explores the roles “quants” or technically trained workers play at hedge funds. I quantify the impact of hiring “quants” on hedge fund strategy and risk taking by exploiting H-1B lottery results and a policy shock that significantly reduced the future supply of foreign high-skilled labor in the U.S. I find that the H-1B visa program allows hedge funds to pursue strategies that are more quantitative (e.g., hedging, systematic trading, etc.) as opposed to fundamentals-based (e.g., event driven). Although there is evidence of substitution between fund strategies, I also find that hedge funds overall become more diversified, both in terms of regional focus and fund investment style.

Traditional theories on corporate financial policy mostly focus on firm-specific determinants and assume that capital structure choices are made independently of the actions of peer firms. The second chapter of my dissertation introduces a theoretical model that embodies the dynamic of how peer behavior affects a firm’s optimal choice of debt level. Specifically, peer effect is captured by a measure of how much the liquidation value of assets are adversely impacted if peer firms choose to liquidate simultaneously, the effect of which is more prominent for industries with highly specialized assets – hence low asset redeployability. The theoretical model predicts that a firm’s optional debt level is lower when the liquidation prices of its assets are expected to be more severely impacted by the concurrent liquidation of peer firms. Moreover, in those situations where peer firms’ cash flows are more closely correlated, the optional debt level will be higher. Individual firms’ attempts to maximize their own utility lead to an industry-wide over leverage, further exacerbating the risk of general crises in an already highly correlated industry.

The third chapter of my dissertation explores the potential of using deep learning models to enhance equity trading strategies such as momentum and reversal trading. A deep feed forward neural network model (DFN) is fed with a training data set (1965-2000) that uses the rolling Z-scored cumulative returns of various horizons as predicative variables. With the model parameters generated by the training set, the model is applied to the validation set (2000-2016) to generate a signal for each stock predicting its likelihood of outperforming the cross-sectional medium. Based on this model-predicted signal, I have constructed an long-short investment portfolio out of the validation data set. The deep learning portfolios yield an annualized return of over 20 percent and generate significantly large alphas over commonly used factor models.

We analyze incentive-efficient government bailouts within a canonical model of intra-firm moral hazard. Bailouts exacerbate the moral hazard of firms and managers in two ways. First, they make them less averse to failing. Second, the taxes to fund bailouts dampen their incentives. Nevertheless, if third-party externalities from keeping the firm alive are strong, bailouts can improve welfare. Our model suggests that governments should use bailouts sparingly, where social externalities are large and subsidies small; eliminate incumbent owners and managers to improve a priori incentives; and finance bailouts through redistributive taxes on productive firms instead of forcing recipients to repay in the future.

We consider a firm with two investment projects (divisions) each run by a manager who can provide (i) (unverifiable) information about the quality of either or both projects and (ii) (unverifiable) access to valuable resources that can enhance the cash flows of either or both projects. We then examine the extent to which capital allocation and managerial compensation schemes can be designed jointly to mitigate the information and incentive problems within the firm. We find that the firm underinvests in capital in both projects (relative to the first-best level), optimal managerial profit-sharing increases in the quality of both projects, and managers underinvest resources in both projects. Interestingly, the underinvestment problem in one project is less severe when the other project is higher quality. This effect will be stronger when the moral hazard problem is more acute and project returns are more highly correlated. We also predict that multi-division firms will invest more (less) than single-division firms when other divisions in the firm perform well (poorly).

How does information, dispersed among diverse geographic markets, customer segments and employees, get incorporated into the stock price? I have endeavored to create novel datasets that incorporate information from thousands of disaggregated investors, employees and customers. I utilize these datasets to construct aggregate measures of information and study how the information measures affect future returns and corporate events.

Specifically in the first chapter titled "Information Aggregation and Asset Prices", I utilize a unique data set based on Google Trends to construct a search index and use it to proxy for the information seeking behavior of retail investors. I find that abnormal search index predicts future buying pressure on the stock of a company. The portfolio with the highest increase in the search index has positive and significant alphas. The search index also predicts earnings surprises and is associated with the pre-earnings announcement drift. My results are robust to alternative specifications of CAR windows, past returns, news coverage, information available to investors prior to the release of earnings numbers, and the information environment surrounding the earnings announcements. Overall, my results are in line with the hypothesis that retail investors' trades have information content relevant to stock prices.

In the second chapter titled "Effect of Employee Satisfaction on Earnings Surprises", I use a unique data set drawn from self administered employee surveys for 1495 US public corporations. I construct an Employee Satisfaction Index (ESI) and use it as a proxy for employee satisfaction. I find that ESI is higher for larger firms, high market to book ratio firms and firms that have low leverage. I also look at the effect of the changes in ESI on quarterly earnings announcements. I find that the changes in ESI are positively and (weakly) significantly related to the future quarterly earnings surprises. Moreover, the effect is stronger for companies that have higher information asymmetries and are more human capital dependent. The results are consistent with the theories that state that employees are insiders in a company and have information relevant to the future corporate performance. Moreover, consistent with human-capital centric theories I find evidence that the change in employee satisfaction has a greater effect on the performance of human-capital dependent companies.

In the final chapter titled "Private Equity Ownership and the Performance of Reverse Leveraged Buyouts", I study the effect of private equity exit on the target firm performance. Using a hand collected sample of 133 reverse leveraged buyout firms from 1997-2002, I examine the financial performance of the firms immediately before the IPO and up to four years after the IPO. I find that for three years after the IPO they continue to outperform their industries. However, performance deteriorates after the IPO. Cross-sectional regression at time of the IPO suggests that long term performance after the IPO is related to changes in ownership by the private equity sponsors and is not related to changes in ownership by other insiders (all officers or directors who are not PE sponsors) or change in leverage. Even after the IPO, I find the positive relation between PE sponsor ownership and future performance continues. To establish causality between PE sponsor ownership and future performance I use the 2SLS-IV approach. The identifying instrument is the number of years since LBO and is a proxy for impatience of PE sponsors to free up their capital. I find that an IV regression finds a weakly significant relation between PE sponsor ownership and future performance.