Determinants of NFL Spread Pricing: Incorporation of Google Search Data Over the Course of the Gambling Week
By Shiv S. Gidumal and Roland D. Muench
We investigate the factors that Las Vegas incorporates into opening spreads for NFL matchups. We include a novel proxy measure for gambler sentiment constructed with Google search data. We then investigate whether changes in this proxy are reflected in the closing spreads for NFL matchups and find that they are incorporated. We also reveal bettors’ preferences for highly visible teams and teams performing well recently. Lastly, we show that the factors that matter in the actual outcome of a game are home field advantage, average points scored for and against, and, most interestingly, our proxy measure for gambler sentiment.
Advisor: Michelle Connolly, Emma Rasiel | JEL Codes: G14, G17
The Impact of Fossil Fuel Prices on Alternative Energy Stocks
By Roman Milioti
The purpose of this paper is to determine if fossil fuel price fluctuations can influence the price alternative energy stock valuations. Employing a Lag Augmented VAR analysis, the research analyzes how natural gas and WTI oil prices impact the price of an alternative energy index. The analysis reveals that neither the price of natural gas nor the price of WTI have a statistically significant positive impact of the price of the alternative energy index. The results are attributed to natural gas and alternative energy acting as both substitutes and compliments given renewable
energy intermittency.
Advisor: Gale Boyd, Kent Kimbrough | JEL Codes: G12, Q42
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.
Advisor: Edward Tower | JEL Codes: G11, G14 Tagged: Information on Market Efficiency, Investment Decisions, Portfolio Choice
An Economic Approach to Evaluating the Impact of AML/CFT Regulations
By Caitlin McGough
This paper addresses the unintended consequences of AML/CFT regulations, seeking to provide an economic analysis of the drivers of de–risking and the broader consequences for the goal of financial integrity. Looking at qualitative data, this paper (1) concludes the problem of de–risking warrants a reconsideration of the enforcement approach and (2) recommends reorienting the banks’ payoff matrix by reducing the cost of compliance and regulatory risk associated with providing financial services to high–risk, low–profit customers. This paper culminates with the recommendation to consider tolerating “honest mistakes” on the part of financial institutions in order to achieve the goals of integrity and inclusion in the international financial system.
Advisor: Connel Fullenkamp | JEL Codes: G18 | Tagged: De-Risking, Financial Inclusion, Money Laundering, Terrorism Financing
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?
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.
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.
Advisor: Per Olsson, Michelle Connolly | 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.
Advisors: Andrew Patton, Scott Schmidler | 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.
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.
Advisor: Andrew Patton, Michelle Connolly | JEL Codes: C32, C51, G1, G12, G17 | Tagged: Beta, Asset Pricing, Dynamic Correlation, Equity, U.S. Markets
