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Category Archives: G1

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

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

Google Search Volume Index: Predicting Returns, Volatility and Trading Volume of Tech Stocks

By Rui Xu

This paper investigates the efficacy of using Google Search Volume Index (SVI), a publicly available tool Google provides via Google Trends, to predict stock movements within the tech sector. Relative changes in weekly search volume index are recorded from April 2004 to March 2015 and correlated with weekly returns, realized volatility and trading volume of 10 actively traded tech stocks. Correlations are drawn for three different time periods, each representing a different stage of the financial business cycle, to find out how Search Volume Index correlates with stock market movements in economic recessions and booms. Google SVI is found to be significantly and positively correlated with trading volume and weekly closing price across 2004 to 2015, and positively correlated with realized volatility from 2009-2015. There exists a positive correlation between weekly stock returns and SVI for half of the stocks sampled across all 3 periods. The regression model was a better fit before and during the recession, suggesting the possibility of stronger “herding” behavior during those periods than in recent years.

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Advisor: Edward Tower | JEL Codes: G1, G14, G17 | Tagged: Analysis, Information, market efficiency, Stock Returns

Multiples Valuation and Abnormal Returns

By Joon Sang Yoon

I investigate whether three commonly used valuation multiples—the Price-to-Earnings Ratio, the EV-to-EBITDA multiple, and the EV-to-Sales multiple—can be used to identify mispriced securities. I find that multiples are successful in identifying mispricing in both the equal and value weighted portfolios relative to the One-Factor CAPM. I further find, after controlling for size and value effects, that the bulk of the abnormal returns are concentrated in smaller firms. Moreover, the Sales multiple seems to outperform the other two multiples in the equal weighted design. In the value weighted design, however, the P/E ratio outperforms the others.

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Advisor: Per Olsson | JEL Codes: G12, G14, M4 | Tagged: Equity Valuation, Long-run Abnormal Returns, Market Efficiency, Multiples Valuation

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

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.

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Advisor: Jia Li | JEL Codes: G02, G15, G20, G21 | Tagged: Word-of-Mouth, Product Differentiation, Herding Behavior

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.

Honors Thesis

Data Set

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 Anomoly: A Model of Interacting Agents

By Phillip Hogan

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.

Honors Thesis

Advisor: Craig Burnside, Michelle Connolly | JEL Codes: G1, G13, G15 | Tagged: Exchange Rates, Forward Premium Anomaly


Undergraduate Program Assistant
Jennifer Becker

Director of the Honors Program
Michelle P. Connolly