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Category Archives: G11

Comparing the Performance of Active and Passive Mutual Funds in Developing and Developed Countries

By Nalini Gupta  

This paper seeks to test the hypothesis that developing countries or informationally inefficient countries should see higher returns for active mutual funds on average than passive funds and the trend should be reversed in developed nations or informationally efficient economies. This analysis is done using a cross section of eight countries, four developed and four developing. Using a fund universe of 20 active and 20 passive funds per country and controls such as volatility, market return, financial market development and Human Development Index among others, we see that there is no clear systematically dominant strategy between active and passive investment universally. While developing countries are associated with lower returns, we do not find a significant difference between active and passive based on development classification. A key finding is that an increase in liquidity, acting as proxy for informational efficiency, leads to a co-movement of active and passive returns in each country. The paper also lends itself to further analysis regarding confounding factor such as noise trading and movement of foreign capital which impact the effect of increased liquidity on mutual fund returns.

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Advisors: Professor Connel Fullenkamp, Professor Kent Kimbrough | JEL Codes: G1, G11, G14

Determining the Drivers of Acquisition Premiums in Leveraged Buyouts

By Peter Noonan   

This thesis analyzes factors that determine acquisition premiums paid by private equity firms in public to private leveraged buyouts. Building off of established literature that models the acquisition premiums paid in corporate mergers and acquisitions (M&A), this paper considers factors that influence a private equity firm’s willingness to pay (referred to as reservation price) and the bargaining power dynamic between a target company and acquirer in leveraged buyouts. Specifically, multivariable regression analysis is used to quantify the impact of a target company’s trading multiple, profitability, stock price as a percentage of its annual high, and number of competitors, a private equity firm’s deal approach and payment method, and the financial market’s 10-year US Treasury yield and high-yield interest rates at the time a transaction was announced. A sample of 320 public to private leveraged buyout transactions completed from 2000 to 2020 is constructed to perform this paper’s regression analysis. Using 2008 as an inflection point, this thesis then applies the same regression model to the subperiods from 2000–2008 and from 2009–2020 to examine how these drivers have changed as a result of industry trends—increased competition, low interest rates, and new value creation investment strategies—as well as the 2008 financial crisis and US presidential election—two crucial events that caused tremendous change in the financial system and intense scrutiny of the private equity industry. From the same original transaction screen, a second sample of 659 transactions is used to perform a difference of acquisition premium means t-test to analyze how the absolute magnitude of leverage buyout acquisition premiums have changed across these two subperiods. The second sample consists of more transactions due the t-tests less data-demanding nature as a result of its fewer variables. Results of this paper’s baseline model suggest that acquisition premiums are driven by a target company’s…

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Advisors: Professor Ronald Leven, Professor Michelle Connolly | JEL Codes: G3, G11, G34

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

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

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.

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Advisor: Andrew Patton | JEL Codes: C1, C11, C58, G11 | Tagged: Bayesian Analysis Global Markets Mean-Variance Portfolio Optimization

Variance Risk Premium Dynamics: The Impact of Asset Price Jumps on Variance Risk Premia

By Jackson Pfeiffer

This paper utilizes the high-frequency stock price data and the corresponding daily option price data of several highly capitalized corporations in order to investigate the impact that asset price jumps of individual equities have on the equities’ respective variance risk premia. The findings of this paper describe many characteristics of the variance risk premia of individual equities, supporting some expectations of the characteristics, and refuting others. In the process of investigating these characteristics, this paper proposes a simple estimator for the market price of the variance risk of an individual equity.

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Advisor: George Tauchen | JEL Codes:  G1, G19, G11 | Tagged: Variance Risk, Variance Swaps, Price Jumps

Enhanced versus Traditional Indexation for International Mutual Funds: Evaluating DFA, Wisdom Tree and RAFI PowerShares

By Heehyun Lim

This paper uses stye analysis to compare the performance of traditional international index funds and enhanced international index funds. It attempts to measure the value added beyond classic indexation by the consideration of fundamentals. By employing Sharpe’s style analysis, I formulate a synthetic portfolio composed of DFA traditional funds to imitate each enhanced index fund portfolio’s performance. Then I compare the return and volatility of each portfolio. The result shows that half of enhanced fund portfolios tested in the paper outperforms their traditional synthetic portfolio, while the other half under-perform.

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Advisor: Edward Tower | JEL Codes: G11, G15 | Tagged: Enhanced Index Fund, Fundamental, Indexation, Style Analysis

Are Asset Allocation Funds Good at Market Timing?

By Yunze Chen

“Don’t forget that your incredible success in consistently making each move at the right time in the
market is but my pathetic failure in making each move at the wrong time. … … I don’t know anyone who can do it successfully, nor anyone who has done so in the past. Heck, I don’t even know anyone who knows anyone who has timed the market with consistent, successful, replicable results.” (John Bogle, quoted in The Finance Buff, 2011).

John C. Bogle, the founder of the Vanguard Group, has long insisted on the superiority of index funds over actively managed mutual funds and the foolishness of attempts to time the market. He published two articles in the Journal of Portfolio Management showing that in eight out of nine style categories, managed mutual funds had lower risk-adjusted returns than the corresponding indexes did. While this demonstrates the failure of stock picking by mutual funds to serve investors well, it says nothing about their ability to time the market by changing styles. Managers of asset allocation funds often use a flexible combination of stocks, bonds, and cash; some, but not all, shift assets frequently based on analysis of business-cycle trends. To test his view of market timing, we evaluated the returns of 82 major asset allocation funds by comparing them with the returns of corresponding baskets of Vanguard’s index funds over a 13-year time span. We find that on average the index funds have higher risk-adjusted returns. We conclude that “simplicity is the ultimate sophistication” applies to mutual fund investments.

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Advisor: Edward Tower | JEL Codes: G10, G11, G20 | Tagged: Expense Ratio, Mutual fund families, Performance

Examination of Time-Variant Asset Correlations Using High- Frequency Data

By Mingwei Lei

Drawing motivation from the 2007-2009 global financial crises, this paper looks to further examine the potential time-variant nature of asset correlations. Specifically, high frequency price data and its accompanying tools are utilized to examine the relationship between asset correlations and market volatility. Through further analyses of this relationship using linear regressions, this paper presents some significant results that provide striking evidence for the time-variability of asset correlations. These findings have crucial implications for portfolio managers as well as risk management professionals alike, especially in the contest of diversification.

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Advisor: George Tauchen | JEL Codes: G, G1, G10, G11, G14 | Tagged: Asset correlations, Diversification, Financial Crisis, High-Frequency Data, Market Volatility, Time-Variant Correlations, Time-Variant Volatility

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