Browsing by Subject "Finance"

This paper examines whether deferred tax asset valuation allowance growth, as a measure of expected future performance, aggregated at the macroeconomy level, conveys information about future GDP growth. Using hand-collected tax footnote data from publicly traded firms over the 1993 to 2019 period, I find that quarterly aggregate valuation allowance growth is negatively associated with future GDP growth up to four quarters ahead. This relationship is incremental to existing accounting and macroeconomic GDP growth indicators, especially for forecast horizons longer than one quarter when other indicators are uninformative. Additionally, the findings suggest that aggregate valuation allowance growth provides unique information that cannot be obtained from other sources of management information, such as management forecasts, the allowance for doubtful accounts, banks’ loan loss provision, and goodwill impairment loss. The findings further indicate that the documented association of GDP growth and aggregate valuation allowance growth is driven by the corporate profit growth component of GDP growth. Collectively, the evidence indicates that aggregate valuation allowance growth provides incremental forward-looking information about GDP growth.

This paper intends to present my summary of internship and some academic individual and team projects, including a quantitative and statistical analysis of some important Macro factors and financial models, and a data analysis project in drug cost reduction. The first chapter discusses the mechanism and impact of pass-through from the dynamics of RMB exchange in China, and the method I used here is the basic econometrics regression analysis. The result is significant and coincides with our common sense when we make investment decisions. The second chapter is about the revised CCAPM model. Through a modified distribution of error terms, CCAPM model will show an improved explanation power. The third chapter is a data analysis project of drug cost reduction. I used Bayesian method to explore the relationship between drug cost and other predictors, and the result gives us advice on designing health plans to minimize the cost.

The use of dynamic factor analysis in statistical modeling has broad utility across an array of applications. This paper presents a novel hierachical structure suited to a particular class of predictive problems - those which necessitate the aggregation of numerous forecasts in the presence of substantial data missingness and a need for systematic dimensionality reduction. The model hierarchy is presented in the context of the prediction of U.S. Nonfarm Payrolls, a well-known economic statistic, though can be generally applied for any context exhibiting an analagous data structure.

This dissertation consists of three essays on asymmetric correlations in financial markets. In the first essay, I have two main contributions. First, I show that dividend growth rates have symmetric correlations. Second, I show that asymmetric correlations are different than correlations being counter-cyclical. The correlation asymmetry I study in this dissertation should not be confused with correlations being counter-cyclical, i.e. being higher during recessions than during booms. I show that while counter-cyclical correlations can simply be explained by counter-cyclical aggregate market volatility, the correlation asymmetry with respect to joint upside and downside movements of returns are not just due to the heightened market volatility during those times.

In the second essay I present a model in order to explain the correlation asymmetry observed in the data. This is the first paper to offer an explanation for observed correlation asymmetry. I formalize the explanation using an equilibrium model. The model is useful to understand both the cross-section and time-series of correlation asymmetry. By the means of my model, we can answer questions about why some stocks have higher correlation asymmetry, and why the correlation asymmetry was higher during 1990s? In the model asset prices respond the realization of dividends and news about the future. However, price responses to news are asymmetric and this asymmetry is endogenous. Price responses are endogenously stronger conditional on bad news than conditional on good news. This asymmetry also generates the observed correlation asymmetry. The price responses are asymmetric due to the ambiguity about the news quality. Information about the quality of the signal is incomplete in the sense that the exact precision of the signal is unknown; it is only known to be in an interval, which makes the representative agent treat news as ambiguous. To model ambiguity aversion, I use Gilboa and Schmeidler (1989)'s max-min expected utility representation. The agent has a set of beliefs about the quality of signals, and the ambiguity-averse agent behaves as if she maximizes expected utility under a worst-case scenario. This incomplete information about the news quality, together with ambiguity-averse agents, generates an asymmetric response to news. Endogenous worst-case scenarios differ depending on the realization of news. When observing ``bad" news, the worst-case scenario is that the news is reliable and the prices of trees decrease strongly. On the other hand, when ``good news" is observed, under the worst-case scenario the news is evaluated as less reliable, and thus the price increases are mild. Therefore, price responses are stronger conditional on a negative signal and this asymmetry creates a higher correlation conditional on a negative signal than conditional on a positive signal. I also show that the results are robust to the smooth ambiguity aversion representation.

Motivated by the model, I uncover a new empirical regularity that is unknown in the literature. I show that correlation asymmetry is related to idiosyncratic volatility: the higher the idiosyncratic volatility, the higher the correlation asymmetry. This novel empirical finding is also useful to understand the time-series and cross-sectional variation in correlation asymmetry. Stocks with smaller market capitalizations have greater correlation asymmetry compared to stocks with higher market capitalization. However, an explanation for this finding has been lacking. According to the explanation offered in this paper, smaller size stocks have greater correlation asymmetry compared to bigger size stocks because small size stocks tend to have higher idiosyncratic volatilities compared to bigger size stocks. In the time-series, correlation asymmetry shows quite significant variation as well. The average correlation asymmetry is especially high for the 1990s and decreases significantly at the beginning of the 2000s. This pattern in times-series can also be explained in terms of the time-series behavior of idiosyncratic volatilities. Several papers including Brandt et al. (2010), document higher idiosyncratic volatilities during 1990s while the aggregate volatility stays fairly stable. Basically, the high idiosyncratic volatilities during the 1990s also caused greater correlation asymmetry.

In the third essay, I study the correlation of returns in government bond markets. Similar to the findings in equity markets, I show that there is some evidence for asymmetric correlations in government bond markets. First, I show that the maturity structure matters for correlation asymmetry in bonds markets: Unlike long-maturity bonds, shorter-maturity bonds tend to have asymmetric correlations. Second, I show that the correlation asymmetry observed in European bond markets disappears with the formation of a common currency area. Lastly, I study the correlation between equity and bond returns in different countries. For long-maturity bonds, correlations with the domestic equity returns are asymmetric for half of the countries in the sample, including the U.S. These findings show that results on asymmetric correlations from equity markets can generalize, at least to some extent, to other financial markets.

Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. In this dissertation, we present a novel framework entitled Bayesian predictive decision synthesis (BPDS) which advances conceptual and theoretical foundations in the intersection of model uncertainty and decision theory. We define new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian predictive synthesis (BPS) and empirical goal-focused model uncertainty analysis. Specifically, we focus on the utilization of a specific outcome-dependent weight function in combination with more traditional model averaging methods that incorporate model performance. This outcome-dependent weight function is enabled by the development of a novel subjective Bayesian perspective on model weighting in predictive decision settings, with theoretical connections to Entropic Tilting and generalized Bayesian updating. We include multiple in-depth case studies from applied contexts to illustrate the use cases of BPDS and raise and investigate relevant questions. These case studies include applications in both the case where predictions depend on the decision at hand and the case where the decision has no impact on predictions. In the decision-dependent case, we present an optimal design for regression prediction and a collaboration involving macroeconomic forecasting. In the decision-independent case, we focus on a setting of sequential time series forecasting for financial portfolio decisions. Overall, these case studies are able to demonstrate the potential for BPDS to improve decisions and thus realized outcomes.

The advent and generalization of digital computing machines in the twentieth century spelled a wholesale revolution in communication technologies. As with every technological revolution, this one came irreducibly co-involved with transformations in the economic sphere. In the latter half of the twentieth century, this meant financialization, which comprised an exponential expansion in the size and power of finance, a reversal in preeminence between the “real” and the financial economy, and the saturation of an increasing proportion of social processes with financial logics and evaluative criteria. None of these developments would have been possible without digital computers. This dissertation, Cash Flows: A Media Archeology of Financial Engineering, 1958-1987, details the involution between late-twentieth century finance and digital media, demonstrating that to understand finance, we must understand the digital, and to understand the digital, we must understand finance.Financialization and the computerization of finance can be accounted for, I show, under the rubric of financial engineering, a distinct mode of financial operation that emerged beginning in 1958. Financial engineering combines a novel conceptual scheme with novel technologies. The conceptual scheme, no-arbitrage, takes over for earlier neoclassical understandings of economic and financial operation based on equilibrium; it shifts the responsibility for the maintenance of “true” prices from the counterbalancing forces of spontaneous rational actors to the concerted efforts of financial theorists. Concomitantly, I show, it uses digital computers to transform financial theory from a post facto, empirical, and descriptive venture to an ex ante, speculative, and constructive one. Financial theory becomes a real-time data processing operation. Drawing on media theory and the philosophy of technology and updating the foundational account of human-machine interaction offered by the French philosopher of technology Gilbert Simondon, Cash Flows offers a way to understand how media transformation drove financialization, what financialization tells us about media transformation, and what consequences we should expect from the continued generalization of digital technologies. The revolution of the digital, as I detail, involves a profound change in the relationship between machines and symbols. Rather than simply reproducing symbols—a capacity machines had possessed since at least the fifteenth century—digital machines operationalized them. As Cash Flows argues, the fundamental factor in last century’s financial transformation was the newfound ability of machines to transform symbols into actions. Digital computers enabled finance to machinically operationalize social and subjective processes humans carry out as a matter of course. Concomitantly, then, the saturation an increasing proportion of social processes by digital media is equally finance’s conquest of the social. All the consequences we see within the finance narrowly defined—from the construction of totalizing interconnection between elements that had previously been understood as belonging to separate spheres; to the transformation of psychic and social flows into value-generating data streams; to the enlistment of the human as a co-processor of digital data; to more frequent, more widely ranging, and more severe crises amplified by feedback loops between humans and machines—are equally the social consequences of digitization.

This dissertation investigates the role of the secondary market in the enforcement of a creditor's security interest. In the first chapter, I examine how creditors respond to ex-post higher foreclosure costs. I find that when repossessing collateral becomes costly, creditors choose to sell their delinquent debt on the secondary market rather than renegotiate with borrowers. Only when repossession becomes prohibitively expensive, thus impeding sale, do creditors offer forbearance. These results highlight a novel channel to explain the lack of mortgage renegotiation, namely, the very presence of a robust secondary market. Subsequent chapters further explore this relationship. In the second chapter, I study how banks respond to riskier collateral enforcement ex-ante. I find that banks exposed to enforcement risk reduce lending for portfolio loans, which are precisely those loans that banks are liable to enforce. In the third chapter, I study how creditors respond to the delayed sale of delinquent debt. I find that suspending the early sale of delinquent debt reduces the incidence of forbearance, increases foreclosures, and limits creditor incentives to cure loans.

This dissertation investigates computational methods for macroeconomic asset pricing models. It demonstrates that advances in economic modeling often require advances in computation and highlights a particular case where more demanding computational methods are required to solve an economic model. It also discusses advances in computational technology that allow researchers to utilize solution methods that would have been previously infeasible. In particular, it demonstrates the wide applicability and potential gains of GPU computing, a parallel computing framework, and applies those tools to a computationally challenging model which investigates trading volume in a general equilibrium, complete-markets economy where agents have heterogeneous beliefs.

This dissertation includes two parts. The first part examines the preventive effect of hedge fund activism against corporate policy deviations. Using stock liquidity and mutual fund fire sales as instruments, I find that when the likelihood of hedge fund activism increases, firms respond by paying shareholder more and CEOs less, holding less cash and leveraging more, and increasing investment into research and development while cutting capital expenditures. These results imply that hedge fund activism has a stronger and broader impact on corporate policy than previously documented. The second part critically examines capital flow-induced mutual fund trades as an exogenous proxy for changes in stock price. I find that liquidity-strapped mutual funds sell widely across all portfolio holdings but the extreme capital outflows could be driven by the performance of portfolio holdings in the first place.

This dissertation presents the construction procedure of “high-frequency” daily measure of changes in housing valuations, and analyzes its return dynamics, as well as investigates its relationship to capital markets. The dissertation consists of three chapters. The first chapter introduces the house price index methodologies and housing transaction data, and reviews the related literature. The second chapter shows the construction and modeling of daily house price indexes and highlights the informational advantage of the daily indexes. The final chapter provides detailed empirical and theoretical investigations of housing index return volatilities.

Chapter 2 discusses the relationship of the housing market with the other markets, such as consumer good market and financial markets. Different housing price indexes and their construction methodologies are introduced, with emphases on the repeat sales model and S&P/Case Shiller Home Price Index. A detailed description of the housing transaction data I use in the dissertation is also provided in this chapter.

Chapter 3 is co-authored with Professor Tim Bollerslev and Professor Andrew Patton. We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity. The correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are commensurate with existing empirical evidence for existing monthly and quarterly house price series. Timely and accurate measures of house prices are important in a variety of applications, and are particularly valuable during times of turbulence, such as the recent housing crisis. To quantify the informational advantage of our daily index, we show that a relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data.

Chapter 4 investigates the properties of housing index return volatilities. Similar to stock market volatility, housing volatilities are found to respond asymmetrically to negative and positive returns. A direct test of volatility on changes in loan-to-value ratio suggests that the observed volatility asymmetry does not stem from changes in degree of housing financial leverage, but could result from the risk premium carried by housing volatility, which is supported by a consumption-based asset pricing model with housing. Moreover, housing and stock volatilities are found to be positively correlated from a set of predictive regressions based on realized variances of housing and stock markets, in which higher (lower) volatility in one market will be followed by higher (lower) volatility in the other. Finally, housing and stock cross-sectional return dispersions are shown to contain useful information in predicting both within-market and cross-market realized volatilities.

This thesis examines how various agency frictions affect corporate financing, capital budgeting, managerial compensation and investment in dynamic settings. In the internal capital budgeting process, the agency issues considered are (i) the division manager privately observes project arrival and quality, and (ii) he can divert allocated capital. The optimal capital budgeting and compensation policies are jointly designed to mitigate agency costs that are endogenously determined. When the division's financial slack is low, positive NPV investments are possibly forgone and manager's pay-performance sensitivity is kept small. When the division's financial slack is high, projects are funded more efficiently and steeper incentives are provided. In the process of external financing, the key friction considered is that the agent has persistent private information about firm performance. In the optimal contract, the firm is financed by outside equity and a credit line contingent on compliance with a cash flow covenant. The agent is compensated via a combination of equity and stock options. As the level of persistence increases, the agent holds less equity and more stock options; the investors hold more equity. Investment is possibly efficient in the constrained firm and is varying with cash flow in the unconstrained firm.

In this dissertation I study the role of limited commitment in dynamic models. In the first part, I consider firms that face uncertainty shocks in a principal-agent setting but have only limited ability to commit to long-term contracts. Limited commitment firms expedite payments to their managers when uncertainty is high, a finding that helps to explain the puzzling large bonuses observed during the recent financial crisis. In the second part, I examine a dynamic investment model where firms invest in a risky asset but cannot hedge the risk of their investment because they lack the ability to commit to future repayments of debt. Once firms have access to exogenous supply of risk free assets they may, on the aggregate level, invest more in the risky asset because risk free technology allows them to grow richer in equilibrium. This result helps to explain the asset price booms in emerging countries when those countries experience substantial capital outflow.

This dissertation discusses model and forecast comparison, calibration, and combination from a foundational perspective. For nearly five decades, the field of forecast combination has grown exponentially. Its practicality and effectiveness in important real world problems concerning forecasting, uncertainty, and decisions propels this. Ample research-- theoretical and empirical-- into new methods and justifications have been produced. However, its foundations-- the philosophical/theoretical underpinnings on which methods and strategies are built upon-- have been unexplored in recent literature. Bayesian predictive synthesis (BPS) defines a coherent theoretical basis for combining multiple forecast densities, whether from models, individuals, or other sources, and generalizes existing forecast pooling and Bayesian model mixing methods. By understanding the underlying foundation that defines the combination of forecasts, multiple extensions are revealed, resulting in significant advances in the understanding and efficacy of the methods for decision making in multiple fields.

The extensions discussed in this dissertation are into the temporal domain. Many important decision problems are time series, including policy decisions in macroeconomics and investment decisions in finance, where decisions are sequentially updated over time. Time series extensions of BPS are implicit dynamic latent factor models, allowing adaptation to time-varying biases, mis-calibration, and dependencies among models or forecasters. Multiple studies using different data and different decision problems are presented, demonstrating the effectiveness of dynamic BPS, in terms of forecast accuracy and improved decision making, and highlighting the unique insight it provides.

This dissertation focuses on how economic markets provide channels for influence over government policy. Specifically, I examine three levels of analysis: the household, the financial security, and the foreign state. Economic constraints on government policy are particularly salient in today's financialized economy. Understanding these dynamics helps us forecast what will happen in the future. Getting these forecasts right is important because taxpayers, governments, and investors all have skin in the game of effective use of government resources. To paint a picture of these constraints, my dissertation contains three papers. The first argues that individuals with access to economic insurance are less likely to protest than those without. Using macroeconomic and survey data, I find evidence supporting my theoretical expectations. The second paper turns from household economics to the financial markets for government debt securities. Although the literature shows how governments make certain choices in debt issuance and the pricing dynamics of government bonds, it remains unclear how the ownership structure of debt affects yields. I argue that government bonds with more concentrated ownership structures have higher price volatility, which should incur volatility risk premium as a result. I find evidence supporting my theoretical expectations. This paper speaks to the relationship between debt ownership and power; it matters because governments with more concentrated debt ownership could see higher debt service payments over time. The third paper considers how state actors can use foreign investment as a policy tool. I argue that Chinese actors increase investment in target countries when future policy is more uncertain because investments act as a hedge against the possibility of unfavorable future policy. This runs counter to the traditional narrative, which suggests that foreign investment is more likely when policy is stable. Using a novel cross-national, high-frequency, machine-coded event data set, I find evidence supporting my expectations. My dissertation paints a picture of the breadth of ways that economic markets influence government policy. Governments contend with the economic interests of constituents who can demonstrate publicly, investors who can affect the price of their debt, and other states that can use investment to secure influence over future policy.

This dissertation studies applied econometric problems in volatility estimation and CDS pricing. The first chapter studies estimation of loss given default from CDS spreads for U.S. corporates. This paper combines a term structure model of credit default swaps (CDS) with weak-identification robust methods to jointly estimate the probability of default and the loss given default of the underlying firm. The model is not globally identified because it forgoes parametric time series restrictions that have ensured identification in previous studies, but that are also difficult to verify in the data. The empirical results show that informative (small) confidence sets for loss given default are estimated for half of the firm-months in the sample, and most of these do not include the conventional value of 0.60. In addition, risk-neutral default probabilities, and hence risk premia on default probabilities, are underestimated when loss given default is exogenously fixed at the conventional value instead of estimated from the data.

The second chapter, which is joint work with Andrew Patton and Kevin Sheppard, studies the accuracy of a wide variety of estimators of asset price

variation constructed from high-frequency data (so-called "realized measures"), and compare them with a simple "realized variance" (RV) estimator. In total, we consider over 400 different estimators, applied to 11 years of data on 31 different financial assets spanning five asset classes, including equities, equity indices, exchange rates and interest rates. We apply data-based ranking methods to the realized measures and to forecasts based on these measures. When 5-minute RV is taken as the benchmark realized measure, we find little evidence that it is outperformed by any of the other measures. When using inference methods that do not require specifying a benchmark, we find some evidence that more sophisticated realized measures significantly outperform 5-minute RV. In forecasting applications, we find that a low frequency "truncated" RV

outperforms most other realized measures. Overall, we conclude that it is

difficult to significantly beat 5-minute RV for these assets.