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Category Archives: G11

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

Volatility and Correlation Modeling for Sector Allocation in International Equity Markets

By Melanie Fan

Reliable estimates of volatility and correlation are crucial in asset allocation and risk management. This paper investigates Static, RiskMetrics, and Dynamic Conditional Correlation (DCC) models for estimating volatility and correlation by testing them in an asset allocation context. Optimal allocation weights for one year found using estimates from each model are carried to the subsequent year and the realized Sharpe ratio is computed to assess portfolio performance. We also study cumulative risk-adjusted returns over the entire sample period. Our ndings indicate that DCC does not consistently have an advantage over the other two models, although it is optimal in certain scenarios.

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Advisor: Aino Levonmaa, Emma Rasiel | JEL Codes: C32, C51, G11, G15 | Tagged: Asset Allocation, Dynamic Correlation, Emerging Markets, Volatilita

Do Vanguard ETF Investors Make Good Decisions? – Testing the Bogle Hypothesis

By Meng Xie

John Bogle, the founder of Vanguard, is a notable opponent of frequent ETF trading. We test his
hypothesis that Vanguard investors are not trading ETFs intelligently. A comparison of dollarweighted
and time-weighted returns is the typical method used for assessing investor timing. We
instead employ Sharpe’s style analysis techniques to compare the returns of a portfolio of ETFs
to a basket of standard Vanguard funds that mimics the ETF portfolio’s pattern of returns. We
find that the ETF portfolio underperforms the standard Vanguard funds, providing empirical
evidence supporting Bogle’s view that ETFs are misused.

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Advisor: Edward Tower | JEL Codes: G11 | Tagged: Exchange-traded funds, Investment, Mutual funds, Vanguard

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Michelle P. Connolly
michelle.connolly@duke.edu