By Jacob Epstein
This paper explores the relationship between active mutual fund performance and market dispersion from January 1990 to December 2018. I find a significant positive relationship between dispersion and 4-factor alpha overall, providing some evidence of managerial skill. There are large differences in this relationship by decade and fund selectivity. The results suggest active mutual funds were able to take advantage of stock-picking opportunities during the 1990s and 2000s, particularly the most active subset of funds. However, I find a significant negative relationship between dispersion and alpha for funds in the 2010s, indicating this relationship has changed over time. I discuss several possible explanations for this reversal, which could present interesting avenues for further research.
Advisors: Professor Emma Rasiel | JEL Codes: G1, G12, G23
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;
By Rafal Rokosz
The advent of blockchain technology has created a new asset class named cryptocurrencies that have experienced tremendous price appreciation leading to speculation that the asset class is experiencing an asset bubble. This paper examines the novelty and functionality of cryptocurrencies and potential factors that may lead to conclude the existence of an asset bubble. To empirically evaluate whether the asset class is experiencing an asset bubble the LPPL model is used. The LPPL model was able to successfully identify two of the four crashes within the data set signifying that cryptocurrencies are within an asset bubble.
Advisors: Ed Tiryakian and Grace Kim | JEL Codes: G12, Z00, C60
By Brian Perry-Carrera
With the recent growth in the investment of cryptocurrencies, such as bitcoin, it has become increasingly relevant to understand what drives price formation. Given that investment in bitcoin is greatly determined by speculation, this paper seeks to find the econometric relationship between public sentiment and the price of bitcoin. After scraping over 500,000 tweets related to bitcoin, sentiment analysis was performed for each tweet and then aggregated for each day between December 1st, 2017 and December 31st, 2017. This study found that both gold futures and market volatility are negatively related to the price of bitcoin, while sentiment demonstrates a positive relationship.
Advisor: Grace Kim | JEL Codes: G12, G41, Z00
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
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
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
Auctions as an Alternative to Book Building in the IPO Process: An Examination of Underpricing for Large Firms in France
By John Mekjian
A relevant factor in determining the quality of an initial public offering (IPO) mechanism is the level and variability of underpricing that occurs. The percentage difference between the IPO price and the closing price after one day of trading is a common way to define the “underpricing” of the stock. Although companies may value a small amount of positive underpricing, they certainly want this to be controlled. Both extreme positive and extreme negative underpricing are undesirable for a company. Building off of a paper that found a lower mean and variability of underpricing for firms that use the auction IPO mechanism as opposed to the book building IPO mechanism, this paper argues that auctions are not disadvantaged when only large firms are considered. Although this paper finds that the book building mechanism controls underpricing better than the auction mechanism, the advantage disappears when considering only large firms. This analysis is relevant because, aside from two companies, only small companies have used the auction IPO mechanism in the United States. Due to the lack of auction IPOs in the United States, this paper uses French data in its analysis. By showing that large firms using the auction mechanism are not disadvantaged when compared to large firms using the book building mechanism, this paper attempts to encourage large firms in the United States to consider using the auction method for their IPOs.
Advisor: James Roberts, Marjorie McElroy | JEL Codes: G12, G14, G20, G30 | Tagged:
By Matt LoSardo
In theory a reverse takeover (RTO) should be a viable alternative to initial public offerings (IPO) for private companies looking to access the public capital markets. Since the IPO process can be very timely and include significant costs, both direct and indirect, we analyze reverse takeovers as an alternative method. Recent papers have posed some similar questions, evaluating underpricing and market-timing, which we look to confirm. However, our paper seeks to build on these analyses, with a particular focus on long-term returns for RTO stocks. Overall we find that reverse takeovers can be successfully used instead of IPOs and should be sustainable long-term investments.
Advisor: Edward Tower, Marjorie McElroy | JEL Codes: G12, G24, G32, G34 | Tagged: