The Effect of Minority History on Racial Disparities in the Mortgage Market: A Case Study of Durham and New Haven
By Jisoo Yoon
In the aftermath of the housing market crash, the concentration of subprime mortgage loans in minority neighborhoods is a current and long-standing issue. This study investigates the presence of racial disparities in mortgage markets by examining two cities with contrasting histories of African American and Hispanic establishment: Durham, North Carolina and New Haven, Connecticut. This study examines data by the Home Mortgage Disclosure Act (HMDA), and distills the effect of minority legacy on the perception of racial risk by using econometric instruments to separate the behavior of national lenders and local lenders. The econometric methods allow national lenders to reflect objective risk measures and neighborhood race dynamics, while local lenders reflect subjective attitudes towards certain races. With its longer history of African American presence, Durham shows a positive attitude towards Black borrowers at the local level, while New Haven shows a more favorable attitude towards its Hispanic residents. Nonetheless, racial legacy also materializes as a negative factor in the form of increased residential segregation and spillover effects. Furthermore, a temporal variation analysis of pre- and post-mortgage market reform data affirms the disappearance of racial bias and continued presence of spillover risk in Durham.
Advisor: Christopher Timmins | JEL Codes: C01, G21, J15, R21, R23, R31 | Tagged: Econometrics, Mortgages, Economics of Minorities, Races, Census, Migration, Population, Neighborhood Characteristics, Housing Supply and Market
Dealing with Data: An Empirical Analysis of Bayesian Black-Litterman Model Extensions
By Daniel Roeder
Portfolio Optimization is a common financial econometric application that draws on various types of statistical methods. The goal of portfolio optimization is to determine the ideal allocation of assets to a given set of possible investments. Many optimization models use classical statistical methods, which do not fully account for estimation risk in historical returns or the stochastic nature of future returns. By using a fully Bayesian analysis, however, this analysis is able to account for these aspects and also incorporate a complete information set as a basis for the investment decision. The information set is made up of the market equilibrium, an investor/expert’s personal views, and the historical data on the assets in question. All of these inputs are quantified and Bayesian methods are used to combine them into a succinct portfolio optimization model. For the empirical analysis, the model is tested using monthly return data on stock indices from Australia, Canada, France, Germany, Japan, the U.K.
and the U.S.
Advisors: Andrew Patton, Scott Schmidler | JEL Codes: C1, C11, C58, G11 | Tagged: Bayesian Analysis Global Markets Mean-Variance Portfolio Optimization
Word-of-Mouth Effects in the Holdings and Trading Activities among Canadian Mutual Fund Managers
By Chang Liu
The study tests the word-of-mouth effects among mutual fund managers in Canada with methodology based on a previous study (Hong et al., 2005), with multiple modifications to it such as the method to locate the mutual fund managers. The results confirm the original findings yet with unexpected outcomes. This study demonstrates smaller word-of-mouth effects compared to the original study and reverse word-of-mouth effects in the largest financial city of Canada. The possible interpretations are further discussed in detail, among which a dynamic model of word-of-mouths effects and product differentiation is introduced. The study also discusses the market structure’s possible implications on such dynamic models.
Advisor: Jia Li | JEL Codes: G02, G15, G20, G21 | Tagged: Word-of-Mouth, Product Differentiation, Herding Behavior
Understanding SME Finance: Determinants of Relationship Lending
By Sean Suk Hyun Choi
Much of the existing literature in small and medium-sized enterprise (SME) finance surveys the impact of borrower and lender characteristics on firms’ credit availability, and it has already been established that there is a link between strong firm-bank relationship and higher level of credit availability. In this paper, I focus on what determines the strength of relationship, measured by length and exclusivity. In particular, I was able to build an original metric to gauge the strength of relationship using the inverse value of the number of financial institution that a firm deals with. Using a set of regressions, I confirm the existing theories that size of the firm and type of ownership matters. Small firms and sole proprietorships tend to have longer and more exclusive relationships, which implies their reliance on relationship lending. Firm owner characteristics are shown to be somewhat important, in that it serves as proxies for a given firm’s creditworthiness.
Advisor: Grace Kim, Michelle Connolly | JEL Codes: G02, G21, G30, L14 | Tagged: Asymmetrical information, Credit Rationing, Relationship Lending, SME Finance
Conditional Beta Model for Asset Pricing By Sector in the U.S. Equity Markets
By Yuci Zhang
In nance, the beta of an investment is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors. Therefore, reliable estimates of stock portfolio betas are essential for many areas in modern nance, including asset pricing, performance evaluation, and risk management. In this paper, we investigate Static and Dynamic Conditional Correlation (DCC) models for estimating betas by testing them in two asset pricing context, the Capital Asset Pricing Model (CAPM) and Fama-French Three Factor Model. Model precision is evaluated by utilizing the betas to predict out-of-sample portfolio returns within the aforementioned asset-pricing framework. Our findings indicate that DCC-GARCH does consistently have an advantage over the Static model, although with a few exceptions in certain scenarios.
Advisor: Andrew Patton, Michelle Connolly | JEL Codes: C32, C51, G1, G12, G17 | Tagged: Beta, Asset Pricing, Dynamic Correlation, Equity, U.S. Markets
Market Dynamics and the Forward Premium Anomaly: A Model of Interacting Agents
By Phillip Hogan and Evan Myer
This paper presents a stochastic model of exchange rates, which is used to explain the forward premium anomaly. In the model, agents switch between four trading strategies, and these changes drive the evolution of the exchange rate. This framework is meant to more realistically represent the important market dynamics of exchange rates, as we suspect these to be the cause of the forward premium anomaly. Our simulations of the model indicate two conclusions: (i) many of the statistical regularities observed in currency markets, including the forward premium anomaly, can be thought of as macro-level scaling laws emerging from micro-level interactions of heterogeneous agents, and (ii) the dynamics of estimates of the beta coefficient in tests of UIP are driven by perceived relationships between changes in interest rates and agents’ aggregate views on the value of the exchange rate, which we call the fundamental value. Section I presents an introduction to the topic and section II reviews the relevant literature. Section III provides the theoretical basis of the forward premium anomaly and our model, then the mathematical definition of the model. Section IV presents the results of a typical simulation which section V compares to relevant stylized facts of the currency markets. Sections VI and VII present our results and a conclusion of what we have drawn from the model.
Advisor: Craig Burnside, Michelle Connolly | JEL Codes: G1, G13, G15 | Tagged: Exchange Rates, Forward Premium Anomaly
The Rise of Mobile Money in Kenya: The Changing Landscape of M-PESA’s Impact on Financial Inclusion
By Hong Zhu
M-PESA, the hugely popular mobile money system in Kenya, has been celebrated for its potential to “bank the unbanked” and increase access to financial services. This paper provides evidence to support this idea and explores mechanisms through which this might be the case. It specifically looks at the savings products held by individuals and how this changes in relation to M-PESA use. It then constructs an index for measuring the extent to which individuals are integrated into the formal financial sector. This paper argues that M-PESA’s effect on financial inclusion is a growing phenomenon, which suggests that keeping pace with the rapid evolutions of this mobile money system should be a high priority for researchers. As this paper elucidates, M-PESA has become notably more integrated with the formal financial sector in 2013 as compared to 2009, which holds implications for user behavior.
Advisor: Michelle Connolly, Xiao Yu Wang | JEL Codes: D14, E42, G21, G23, O1, O17, O16, O33 | Tagged: Financial Inclusion, Mobile Money, Savings,Technology
Corporate Financial Distress and Bankruptcy Prediction in North American Construction Industry
By Gang Li
This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt & non-bankrupt firms selected across the timeline of 1985-2013. Another main goal this paper is to explore the predictive power of an expanded variable set tailored to the construction industry and compare the results. Specifically, this measuring process is done using machine learning algorithm based on scikit-learn library that transforms a raw .csv file into clean vectorized dataset. The algorithm provides various classifiers to cross-validate the training set, which produces mixed statistics that favors neither variable set but provides insight into the reliability of the non-linear classifiers.
Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning
The Impact of Population Mobility on repayment Rates in Microfinance Institutions
By Allison Vernerey and Johan Hörnell
Several studies have attempted to model the determinants of repayment rates for group-based loans administered by micro-finance institutions (MFIs). One of the main variables that have been identifies as playing a role in determining the repayment rate is social capital. Empirical research however has struggled with quantifying this qualitative variable, resulting in vast inconsistencies across studies, aggravating cross-comparison and objective interpretation. Instead, we argue that the use of quantitative, cross-country comparable proxy that is intuitively linked to social capital would yield more consistent and reliable results. We hypothesize that population mobility is such a proxy, and that lower population mobility correlates positively with higher social capital and thus higher repayment rates. Using population mobility as a proxy for social capital would allow MFIs to lower their cost of data collection for performance assessments and simplify the process for policy makers trying to evaluate the programs success. At the village level, we find significant evidence that higher emigration within a community is strongly linked to lower repayment rates in micro-finance. These results provide micro-finance institutions with a new and more cost effective way to monitor their performance as well as improve their capacity to make well-informed lending decisions.
Advisors: Genna Miller, Kent Kimbrough | JEL Codes: G, G2, G21 | Tagged: Bangladash, Microfinance Institutions, Population Mobility, Repayment Rates, Social Capital
The Impact of Macroeconomic Surprises on Mergers & Acquisitions for Real Estate Investment Trusts
By John Battinelli and John Reid
This paper examines the impact of various macroeconomics and real estate specific surprises on M&A transactions involving Real Estate Investment Trust. The 2008 financial crisis drastically affected merger & acquisitions activity, especially within the real estate market. The number of M&A transactions involving Real Estate Investment Trusts were very volatile during this period of economic turmoil and it appeared that several economic factors contributed to changing patterns in M&A activity. Our study uses time series data to draw a connection between REIT-related M&A activity and quantifiable factors. From or results we find there to be a relationship between the macroeconomic environment and REIT-related M&A activity.
Advisors: Connel Fullenkamp, Kent Kimbrough, Marjorie McElroy,
JEL Codes: G10, G14, G34 | Tagged: Macroeconomic Surprises, Mergers & Acquisitions, Real Estate Investment Trust
