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Category Archives: G1

The Impact of Post-IPO Private Equity Ownership on Long-Term Company Performance

By Maria Suhail and Cipriano Echavarría

This thesis contributes to existing knowledge of private equity (PE) by analyzing the
impact of PE ownership post-IPO upon the long-term performance of companies. It considers whether companies perform better when PE funds maintain their ownership stakes post-IPO and whether this performance is also impacted by the degree of ownership that is maintained after IPO. This study uses stock performance (measured by cumulative excess stock returns) as a proxy for long-run company performance. The paper constructs and analyzes a sample of 487 companies that underwent an IPO between 2004 and 2012 to determine the implications of the maintenance and level of PE ownership by analyzing the performance of these companies for six years post-IPO. Results suggest that PE ownership post-IPO positively impacts long-term stock performance of companies. Duration and degree of PE ownership post-IPO are also important determinants of long-run performance likely due to the positive signal that continued PE ownership sends to outside investors about the quality of the company, the information asymmetry that exists between public and private markets and that PE firms are experienced managers that add value to companies.

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Advisors: Professor David Robinson, Professor Michelle Connolly | JEL Codes: G11, G14, G24

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
significance.

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

Evaluating Stock and Bond Portfolio Allocations using CAPER and Tobin’s Q

By Jayanth Ganesan

I test whether an investor can increase the returns on their portfolio over the long-term by timing the market using measures of market value, such as the Tobin’s q ratio and the Cyclically Adjusted Price Earnings (CAPE or Shiller-CAPE). To test this proposition, I examine contrarian investor strategies proposed by Smithers and Wright (2000) and investor strategies based on different equity-fixed income combination portfolios. I seek to determine whether these strategies produce higher risk-adjusted returns than buy-and-hold equity strategies such as those proposed by Siegel (2014) for long-term portfolios. I also examine whether Siegel’s theory that stocks are better investment vehicles than bonds for investment horizons greater than 20 years. In my study, buy-and-hold portfolios composed of the S&P 500 have additional annualized returns of 1.5% than portfolios which reallocate funds in alternative securities based on CAPE and q thresholds. I conclude that for long-term investment horizons, an investor is unlikely to increase portfolio returns by reallocating funds to an alternative asset class when stocks are overvalued. However, I do not find that stocks are better investment vehicles compared to bonds as portfolio with bonds have a lower portfolio risk in my sample. I believe that the effectiveness q ratios for market timing is likely to be independent of how the q ratio is calculated. As suggested by Asness (2015), I find that portfolios that utilize both value and trend investing principles with CAPE and q may outperform portfolios that utilize only value-based market timing strategies. I conclude that CAPE and q based timing strategies are difficult to implement without detailed knowledge of future stock valuations.

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Advisor: Edward Tower | JEL Codes: G11, G14 | Tagged: Information on Market Efficiency, Investment Decisions, Portfolio Choice

Where Did The Liquidity Go? The Cost of Financial Regulation to Foreign Exchange Markets

By James Stevenson

In financial markets, the terms “bull” and “bear” markets are used to describe the cyclicality of asset prices. Similar to asset price cycles, there are cycles in regulatory scrutiny. Beginning in the 1980’s, regulatory scrutiny diminished, cumulating in the repeal of the Glass-Steagall Act in 1999, allowing commercial banks and securities firms to be housed under the same roof for the first time since the 1930’s. In the aftermath of the global financial crisis in 2008 and 2009, the tides have reversed on financial regulation. With the Dodd-Frank reforms in the United States, and similar regulation being signed into law around the world, it is unknown how new regulation will affect financial markets. Legislators wrote the new rules in hopes that they would create safer financial institutions, but at what cost?

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Advisor: Connel Fullenkamp | JEL Codes: G1, G12, G18 | Tagged: Dodd-Frank, Financial Regulation, Foreign Exchange, Market Liquidity, Volcker Rule

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