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Tag Archives: market efficiency
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.
Advisor: Professor Brian Weller | JEL Codes: G12; G14; G23;
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 | JEL Codes: G14, G17
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:
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 | JEL Codes: G12, G14, M4 | Tagged: Equity Valuation, Long-run Abnormal Returns, Market Efficiency, Multiples Valuation