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Category Archives: G

Social Capital and Financial Development after Economic Shocks: Evidence from Italy after the Financial Crisis of 2007-2009

By Sujay Rao & Ethan Lampert

Like traditional forms of capital, social capital – an intangible measure of an individual’s social networks, trust in institutions, and participation in civic life – has implications for personal and financial behavior. Individuals from educated, well established backgrounds with fruitful family ties may be more amenable to opening new lines of credit or investing in stock markets due to their trust in and connectedness with society. But what happens after a major economic shock, such as the financial crisis of 2008? Using Italy as a case study and panel data from the Survey of Household Income and Wealth, we find that social capital has significant effects on an individual’s credit card usage, informal borrowing, and choice to invest in securities.

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Advisors: Professor Grace Kim, Professor Michelle Connolly, Professor Giovanni Zanalda | JEL Codes: G01, G2, O1, D1, D14

Leverage and Varying Metrics of Firm Performance

By Preston Jiateng Huang

This paper sets out to examine the effect of leverage on company performance. Drawing on the methodology of key prior research, this study finds that leverage has a consistent negative effect on firm growth; by contrast, no such negative impact was found on return on equity. Importantly, such patterns hold throughout the entire period under study (1970-2017), during which several disruptive economic events have occurred. These results highlight the importance of selecting appropriate company performance measures when studying the effect of debt load on a firm as well as the misalignment of incentives for policymakers and company management. Other implications are also discussed.

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Advisor: Professor Kyle Jurado | JEL Codes: G24; G31; G32

Wrangling the Herd: A Cross-Cultural and Cross-Industry Approach to Herding Market Behavior

By Tyler Fenton and Jarred Kotzin

The traditional efficient market hypothesis serves as the foundation of modern economic theory, governing the investigation of financial markets. While this premise assumes all investors are rational and all information is immediately incorporated into markets, this paper explores herding behavior – a central tenet of behavioral finance that explains the apparent inefficiencies of financial markets. Utilizing return data from the past 10 years from eight exchanges around the world, segmented into 10 industry classes as well as a broad market index, we compare levels of herd behavior using return dispersion proxies. We find significant evidence of herding in nearly all exchanges and all industries included in the study and the degree of this herd behavior varies across industries in different countries. Overall, we find support for the behavioral finance principle of herding and conclude that certain cultural or non cultural factors affect this activity differently in various countries and industries.

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Advisors: Professor Connel Fullenkamp | JEL Codes: G4, G14, G15

Is Smart Money Smart? The Costs of Hedge Funds Trading Market Anomalies

By Matthew J. Farrell

Do hedge funds earn statistically significant premia on common factor trading strategies after trading costs are accounted for? Furthermore, what is the gap between what a hedge fund would earn and the paper portfolios that they hold? I answer this question by using the latest cutting-edge methodology to estimate trading costs for major financial market anomalies. This methodology uses the familiar asset-pricing Fama-MacBeth procedure to compare the on-paper compensation to factor exposures with those earned by hedge funds. I find that the typical hedge fund does not earn profits to value or momentum, and and low returns to size.

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Advisor: Professor Brian Weller | JEL Codes: G12; G14; G23;

Prediction in Economics: a Case Study of Economists’ Views on the 2008 Financial Crisis

By Weiran Zeng

Prediction in economics is the focal point of debate for the future of economics, ever since economists were burdened with the failure to “predict” the 2008 Financial Crisis. This paper discusses positions held by philosophers and economic methodologists regarding what kinds of predictions there are and creates a taxonomy of prediction. Through evaluation of those positions, this paper presents different senses of prediction that can be expected of economics, and assess economists’ reflections according to those senses.

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Advisor: Kevin Hoover | JEL Codes: B41, N1, G17

Evaluating Asset Bubbles within Cryptocurrencies using the LPPL Model

By Rafal Rokosz

The advent of blockchain technology has created a new asset class named cryptocurrencies that have experienced tremendous price appreciation leading to speculation that the asset class is experiencing an asset bubble. This paper examines the novelty and functionality of cryptocurrencies and potential factors that may lead to conclude the existence of an asset bubble. To empirically evaluate whether the asset class is experiencing an asset bubble the LPPL model is used. The LPPL model was able to successfully identify two of the four crashes within the data set signifying that cryptocurrencies are within an asset bubble.

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Advisors: Ed Tiryakian and Grace Kim | JEL Codes: G12, Z00, C60

Effect of Sentiment on Bitcoin Price Formation

By Brian Perry-Carrera

With the recent growth in the investment of cryptocurrencies, such as bitcoin, it has become increasingly relevant to understand what drives price formation. Given that investment in bitcoin is greatly determined by speculation, this paper seeks to find the econometric relationship between public sentiment and the price of bitcoin. After scraping over 500,000 tweets related to bitcoin, sentiment analysis was performed for each tweet and then aggregated for each day between December 1st, 2017 and December 31st, 2017. This study found that both gold futures and market volatility are negatively related to the price of bitcoin, while sentiment demonstrates a positive relationship.

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Advisor: Grace Kim | JEL Codes: G12, G41, Z00

Multi-Horizon Forecast Optimality Based on Related Forecast Errors

By Christopher G. MacGibbon

This thesis develops a new Multi-Horizon Moment Conditions test for evaluating multi-horizon forecast optimality. The test is based on the variances, covariances and autocovariances of optimal forecast errors that should have a non-zero relationship for multi-horizon forecasts. A simulation study is conducted to determine the test’s size and power properties. Also, the effects of combining the Multi-Horizon Moment Conditions test and the well-known Mincer-Zarnowitz and zero autocorrelation tests into one forecast optimality test are examined. Lastly, an empirical study evaluating forecast optimality for four multi-horizon forecasts made by the Survey of Professional Forecasters is included.

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Advisors: Andrew Patton, Grace Kim and Kent Kimbrough | JEL Codes: G1, G17, G00

An Analysis of Passive and Active Bond Mutual Fund Performance

By Michael J. Kiffel

The literature on the performance differential between passively and actively managed equity mutual funds is thorough: passively managed funds generally outperform their active counterparts except in the rare presence of highly-skilled managers. However, there exists limited academic research regarding fixed income mutual funds. This study utilizes the Fama-French bond risk factors, TERM and DEF, in a dual-step multivariate linear regression analysis to determine this performance differential between passively and actively managed bond mutual funds. The funds are comprised of either corporate or government bonds, spanning three categorizations of average maturities. Overall, it is determined that passively managed bond funds offer higher net returns than those offered by actively managed funds. Additionally, the regressions demonstrated that DEF possesses a high degree of predictive power and statistical

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Advisor: Edward Tower | JEL Codes: C55, G10, G11

Modeling Variation in U.S. Bank Holding Companies’ Net Interest Margins

By Daniel Dorchuck

This study explores variation in US bank holding companies’ (BHCs) net inter-est margins (NIMs) and the effects of interest rate risk exposure on NIMs. Interest rate risk (IRR) is intrinsic in maturity transformation and financial intermediation as banks take on short-term liabilities in the form of deposits and create assets in the form of loans with longer maturities and different repricing profiles. Accordingly, interest rate risk is necessary for bank holding companies (BHCs) to be profitable in financial intermediation, and net interest margins are chosen as a variable of inter-est because they are an isolated measure of bank’ profitability from interest earning assets. Naturally, BHCs employ maturity pairing and derivative hedging to mitigate IRR and ultimately increase and smooth earnings. Synthesizing banks’ balance sheet and income statement data, macroeconomic variables, credit conditions, and interest rate environment variables, this study hopes to expand on existing work by provid-ing insight on the determinants of NIMs as well as interest rate derivatives’ efficacy in increasing and stabilizing net interest margins. The models presented establish links between long term rate exposure, risk-averse capital positions, and increased margins. Additionally, the models suggest that banks earn smaller spreads (NIMs) in higher interest rate environments but benefit from steeper yield curves.

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Advisor: Mary Beth Fisher, Kent Kimbrough |  JEL Codes: E44, G20, G21 | Tagged: Depository Institutions, Interest Rate Derivatives, Interest Rate Risk, Net Interest Margins, US Commercial Banking 


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