Browsing by Subject "Economics, Finance"

In Chapter 1, I document a negative (positive) relationship between changes in large (small) blockholders' ownership and abnormal returns. The evidence in this paper suggests that an increase in the relatively large blockholders' ownership raises the consumption of private benefits while an increase in the relatively small blockholders' ownership constrains large blockholders from expropriating minority shareholders. Moreover, I find an inversely U-shaped relationship between changes in the largest blockholders' ownership and firm value. As large blockholders' ownership and control increase, the negative effect of firm value driven by expropriating minority shareholders starts to exceed the incentive benefits of monitoring by the largest blockholder. I also show that the negative relationship between changes in institutional investors' control and abnormal returns declines as analysts' following increases.

In Chapter 2, I study the role of trading as a governance mechanism. I hypothesize that governance through trading plays a significant monitoring role in practice and that engaging in "voice" and "exit" can be substitutes. I show that abnormal turnover following earnings announcements is significantly higher for firms with large institutional blockholders than for those with small individual

shareholders. For firms with majority institutional ownership, I demonstrate that abnormal trading is higher for firms with multiple blockholders than for those with a single large blockholder and that abnormal trading increases with the number of institutional investors and declines with the percent of stocks owned by the

largest institutional investor. Moreover, this excess trading is driven by mutual fund investors, which are non-interventionist and thus are more likely to engage in "exit" than "voice". I also show that for firms with large institutional blockholders, abnormal trading following public announcements increases with liquidity.

The costs and constraints to financing, and the factors that influence them, play critical roles in the determination of corporate capital structures.

Chapter 1 estimates firm-specific marginal cost of debt functions for a large panel of companies between 1980 and 2007. The marginal cost curves are identified by exogenous variation in the marginal tax benefits of debt. The location of a given company's cost of debt function varies with characteristics such as asset collateral, size, book-to-market, intangible assets, cash flows, and whether the firm pays dividends. Quantifying, the total cost of debt is on average 7.9% of asset value at observed levels, reaching as high as 17.8%. Expected default costs constitute approximately half of the total ex ante cost of debt.

Chapter 2 uses the intersection between marginal cost of debt functions and marginal benefit of debt functions to examine optimal capital structure. By integrating the area between benefit and cost functions, net benefit of debt at equilibrium levels of leverage is calculated to be 3.5% of asset value, resulting from an estimated gross benefit of debt of 10.4% of asset value and an estimated cost of debt of 6.9%. Furthermore, the cost of being overlevered is asymmetrically higher than the cost of being underlevered. Case studies of several firms reveal that, for some firms, the cost of being suboptimally levered is small while, for other firms, this cost is large, suggesting firms face differing sensitivities to the capital structure choice.

Finally, Chapter 3 examines the role of financing constraints on intertemporal capital structure choices of the firm via a structural model of capital investment. In the model, firms maximize value by choosing the amount of capital to invest and the amount of debt to issue. Firms face a dividend non-negativity constraint that restricts them from issuing equity and a debt capacity constraint that restricts them from issuing non-secured debt. The Lagrange multipliers on the two constraints capture the shadow values of being constrained from equity and debt financing, respectively. The two financing constraint measures are parameterized using firm characteristics and are estimated using GMM. The results indicate that these measures capture observed corporate financing behaviors and describe financially constrained firms. Finally, between the two financing constraints, the limiting constraint is the debt restriction, suggesting that firms care about preserving financial slack.

The central puzzles in financial economics commonly include

violations of the expectations hypotheses, predictability of excess returns, and the levels and volatilities of nominal bond yields, in addition to well-known equity premium and the risk-free rate puzzles.

Equally surprising is the recent evidence on large moves in asset prices, and the over-pricing of the out-of-the-money index put options relative to standard models. In this work, I argue that the long-run risks type model can successfully explain these features of financial markets. I present robust empirical evidence which supports the main economic channels in the model. Finally, I develop econometric methods to estimate and test the model, and find that it delivers plausible preference and model parameters and provides a good fit to the asset-price and macroeconomic data.

In the first chapter, which is co-authored with Ravi Bansal, we present a long-run risks based equilibrium model that can quantitatively explain the violations of expectations hypotheses and predictability of returns in bond and currency markets. The key ingredients of the model include a low-frequency predictable component in consumption, time-varying consumption volatility and investor's preferences for early resolution of uncertainty. In this model, varying consumption volatility in the presence of the predictable consumption component leads to appropriate variation in bond yields and the risk premia to provide an explanation for the puzzling violations of the expectations hypothesis. Using domestic and foreign consumption and asset markets data we provide direct empirical support for the economic channels highlighted in the paper.

In the second chapter, co-authored with Ravi Bansal, we develop a general equilibrium model in which income and dividends are smooth, but asset prices are subject to large moves (jumps). A prominent feature of the model is that the optimal decision of investors to learn the unobserved state triggers large asset-price jumps. We show that the learning choice is critically determined by preference parameters and the conditional volatility of income process. An important prediction of the model is that income volatility predicts future jumps, while the variation in the level of income does not. We find that indeed in the data large moves in returns are predicted by consumption volatility, but not by the changes in the consumption level. In numerical calibrations, we show that the model can quantitatively capture these novel features of the data.

In the third chapter, I present a long-run risks type model where consumption shocks are Gaussian, and the agent learns about unobserved expected growth from the cross-section of signals. The uncertainty about expected growth (confidence measure), as in the data, is time-varying and subject to jump-like risks. I show that the confidence jump risk channel can quantitatively account for the option price puzzles and large moves in asset prices, without hard-wiring jumps into consumption. Based on two estimation approaches, the model provides a good fit to the option price, confidence measure, returns and consumption data, at the plausible preference and model parameter values.

The first essay of my dissertation investigates how lender-specific shocks impact the strictness of the loan contract that a borrower receives. Exploiting between-bank variation in recent portfolio performance, I find evidence that banks write tighter contracts than their peers after suffering defaults to their own loan portfolios, even when

defaulting borrowers are in different industries and geographic regions than the current borrower. The effects of recent defaults persist after controlling for bank capitalization, although negative bank equity shocks are also strongly associated with tighter contracts. The evidence is consistent with lenders learning about their own screening technology

via defaults and adjusting contracts accordingly. Finally, contract tightening is most pronounced for borrowers who are dependent on a relatively small circle of lenders, with each incremental default implying covenant tightening equivalent to that of a ratings downgrade.

The second essay examines the use of soft information in primary and secondary mortgage markets. Using a large sample of mortgage loan applications, I develop a proxy for soft information collection based on the probability that a mortgage applicant had a face-to-face meeting with a loan officer. I find that the use of soft information increases the probability of loan approval, and conditional on loan approval, reduces the interest rate charged. These loans, however, are less likely to be sold, consistent with the difficulty in credibly communicating soft information. This provides evidence of one mechanism through which securitization may affect screening. Meanwhile, preliminary evidence suggests that screening based on soft information may be valuable to lenders,

with face-to-face meetings substantially reducing the growth in loan delinquencies during the recent period.

In the first chapter of this dissertation I develop a flexible and estimable equilibrium model that jointly considers location decisions of heterogeneous agents across space, and their optimal portfolio decisions. Merging continuous-time asset pricing with urban economics models, I find a unique sorting equilibrium and derive equilibrium house and asset prices in closed-form. Risk premia for homes depend on both aggregate and local idiosyncratic risks, and equilibrium returns for stocks depend on their correlation with city specific income and house price risk. In equilibrium, very risk-averse households do not locate in risky cities although they may have a high productivity match with those cities. I estimate a version of this model using house price and wage data at the metropolitan area level and provide estimates for risk premia for different cities. The estimated risk premia imply that homes are on average about 20000 cheaper than they would be if owners were risk-neutral. This estimate is over 100000 for volatile coastal cities. I simulate the model to study the effects of financial innovation on equilibrium outcomes. For reasonable parameters, creating assets that correlate with city-specific risks increase house prices by about 20% and productivity by about 10%. The average willingness to pay for completing markets per homeowner is between $10000 and $20000. Productivity is increased due to a unique channel: lowering the amount of non-insurable risk decreases the households' incentive to sort on these risks, which leads to a more efficient allocation of human capital in the economy.

The second chapter of this dissertation studies ability signaling in a model of employer learning and statistical discrimination. In traditional signaling models, education provides a way for individuals to sort themselves by ability. Employers in turn use education to statistically discriminate, paying wages that reflect the average productivity of workers with the same given level of education. In this chapter, we provide evidence that graduating from college plays a much more direct role in revealing ability to the labor market. Using the NLSY79, our results suggest that ability is observed nearly perfectly for college graduates. In contrast, returns to AFQT for high school graduates are initially very close to zero and rise steeply with experience. As a result, from very beginning of the career, college graduates are paid in accordance with their own ability, while the wages of high school graduates are initially unrelated to their own ability. This view of ability revelation in the labor market has considerable power in explaining racial differences in wages, education, and the returns to ability. In particular, we find a 6-10 percent wage penalty for blacks (conditional on ability) in the high school market but a small positive black wage premium in the college labor market. These results are consistent with the notion that employers use race to statistically discriminate in the high school market but have no need to do so in the college market.

The first essay of my dissertation introduces a new method for eliciting market beliefs about the expected outcomes of a merger negotiation after announcement. During a merger negotiation, the market prices of the firms involved

reflect beliefs about their values both in the merged and

standalone states, as well as the likelihood of either outcome.

These beliefs determine stock price reactions to news of a possible

merger, but those prices alone do not contain sufficient information

to identify the latent beliefs that they reflect. I develop a new

method which, by using additional data in the form of option prices,

is able to identify these beliefs. This method allows for a clear

decomposition of a negotiating firm's expected value change into two

parts: the value of the transaction to the firm, and new information

about its standalone value. Previous research into estimating

merger synergies has struggled to obtain an appropriate alternative

against which to measure the realized outcome. The market's beliefs

about state-contingent firm values give an estimate of both. Through

a direct comparison of the estimates of a firm's value in both the

merged and standalone states, I obtain a strong, practical measure

of the expected value-creating potential of a merger before its

consummation.

The second essay applies the state-contingent payoff estimation method developed previously to addressing questions about the size effect in mergers. A growing body of evidence indicates that large acquisitions destroy value. However, we do not yet know why. Several theories have been advanced, but their effects are difficult to observe in isolation. It has thus been impossible to tell whether negative post-announcement acquirer returns are caused by market expectations of value-destroying acquisitions or revealed bad news about standalone value. This paper resolves this issue by decomposing expectations about merger outcomes into expected value change from completing the acquisition and revision of beliefs about standalone firm value. The data show that deal size is correlated with value destruction, while acquirer size is correlated with release of unfavorable information. Deal size correlates with value destruction, acquirer size with bad news about the firm. Furthermore, the results suggest that overpayment is a prerequisite for large acquisitions. These findings reduce the set of possible theoretical explanations for the size effect.

The idea that integrates parts of this dissertation is that high-frequency data allow for more precise and robust methods for forecasting financial volatility and elucidating the role of volatility in forming asset prices. Thus, the first two chapters compare the performance of model-free forecasts specifically designed to employ high-frequency data with the performance of "classical" forecasts developed for daily data. The final chapter of the dissertation incorporates high-frequency data to verify the predictions of asset pricing models about the risk-return relationships at the very shortest horizons. The results are arranged in the following order.

Chapter 1 presents the analytical comparison of feasible reduced-form forecasts designed to employ high-frequency data and model-based forecasts updated to use high-frequency data. The prediction errors of both forecast groups are calculated using the ESV-representation of Meddahi (2003), which allows one to generalize the statements from this analysis to a wider class of volatility processes. The results show that reduced-form forecasts outperform model-based forecasts at longer horizons and perform just as well for day-ahead forecasts.

Chapter 2 expands the conclusions from Chapter 1 to economic measures of forecast performance. These performance measures are constructed within a microeconomic framework that mimics the decision making process of a variance trader who uses volatility forecasts to predict the future profitability of a trade. The results support the theoretical predictions of Chapter 1.

Chapter 3 is co-authored with Professor Tim Bollerslev and Professor George Tauchen. It extends the "long-run risk" model of Bansal and Yaron(2004) to consistently price volatility risks and to be applicable to high-frequency data. The hypothesis at the outset is that while financial volatility is a long-memory process (it exhibits long-range dependence), its own variance (volatility-of-volatility) is a short memory one. Then the presented model implies that the volatility premium (the measure of the difference between option-implied and expected variances) should be short-memory as well. This insight is confirmed by studying cross correlations of returns and volatility measures. Horizons at which cross correlations are considered are unique for the literature; they start at intra-day values, as short as five minutes.

Organizational Capital Budgeting Model (OCBM) is a general theory of capital budgeting that incorporates traditional capital budgeting theories and the consideration about firm's information/ organization structure. The traditional financial capital budgeting model is a special case of OCBM. Therefore, OCBM not only broadens the traditional model, but also explains the heterogeneous behaviors of firms using quasi/non-financial version of capital budgeting. I demonstrate the validity of OCBM with multiple research methods. The field studies about Asian conglomerates are carefully constructed. The conglomerates are important dataset to study organizational decision making because of their size, scope, controversial behaviors and global presence.