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Author: Michael Gorman
Publisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 89
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Book Description
Traditional methods of measuring asset pricing anomalies have historically relied on full sample tests of static parameters. With the increase of computational power and data available we are able to allow for time varying factor loadings for a portfolio based on asset rotation and also time varying factors by asset. In the first paper we find that commonly used estimates of time varying asset pricing anomalies contain significant bias. We are able to show that the historical returns used to select momentum portfolios result in biased data in the short window asset level regressions which the literature uses to estimate portfolio parameters. This is caused through a non-random selection criterion which systematically chooses high epsilon assets. These nonrandom epsilons, when regressed upon bias estimates of alpha, and through the correlation structure of the parameters they also bias the estimates of beta. We present a new methodology that is not subject to this bias, and allows for an accurate measurement of the size of anomalies. In executing this we find that inefficient portfolio rotation in the original portfolio level estimates is also indicative of bias. As such we suggest that the new methodology we propose is more accurate and less susceptible to bias than those currently in use in the literature. The new model suggests that to this point the risk adjusted returns of the momentum portfolio have been underestimated in the literature. In the second paper we demonstrate that the momentum anomaly is driven by a small number of assets in the market using our new model and the methodology of False Discovery Rates. We show that these assets, behave differently in long and short portfolios, and also perform differently during the first month reversal period. Finally we demonstrate that an appropriately risk adjusted momentum alpha shows that extreme months are not sufficient to explain away momentum, and that poor returns in extreme months are overstated by traditional methods of measuring momentum. To this extent we claim that market downturns in the last 15 years have been insufficient to effectively eliminate the momentum anomaly as has been suggested.
Author: Michael Gorman
Publisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 89
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Book Description
Traditional methods of measuring asset pricing anomalies have historically relied on full sample tests of static parameters. With the increase of computational power and data available we are able to allow for time varying factor loadings for a portfolio based on asset rotation and also time varying factors by asset. In the first paper we find that commonly used estimates of time varying asset pricing anomalies contain significant bias. We are able to show that the historical returns used to select momentum portfolios result in biased data in the short window asset level regressions which the literature uses to estimate portfolio parameters. This is caused through a non-random selection criterion which systematically chooses high epsilon assets. These nonrandom epsilons, when regressed upon bias estimates of alpha, and through the correlation structure of the parameters they also bias the estimates of beta. We present a new methodology that is not subject to this bias, and allows for an accurate measurement of the size of anomalies. In executing this we find that inefficient portfolio rotation in the original portfolio level estimates is also indicative of bias. As such we suggest that the new methodology we propose is more accurate and less susceptible to bias than those currently in use in the literature. The new model suggests that to this point the risk adjusted returns of the momentum portfolio have been underestimated in the literature. In the second paper we demonstrate that the momentum anomaly is driven by a small number of assets in the market using our new model and the methodology of False Discovery Rates. We show that these assets, behave differently in long and short portfolios, and also perform differently during the first month reversal period. Finally we demonstrate that an appropriately risk adjusted momentum alpha shows that extreme months are not sufficient to explain away momentum, and that poor returns in extreme months are overstated by traditional methods of measuring momentum. To this extent we claim that market downturns in the last 15 years have been insufficient to effectively eliminate the momentum anomaly as has been suggested.
Author: Che Kuan Chen
Publisher:
ISBN:
Category : Business
Languages : en
Pages :
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Book Description
This dissertation investigates the impact of mutual funds in the cross-sectional stock returns and examines a conflict in the existing literature that characterizes momentum. In the first essay, I examine the explanatory power of aggregate mutual fund flows for the profitability of price-based (i.e., momentum and 52-week high) and non-price-based (i.e., earnings surprises, profitability, share issuance, accrual and asset growth) anomalies in the cross-section of returns. I find that the flow-based trading of mutual funds contributes to mispricing as measured by the profits to price-based anomalies, especially at times when market-wide funding costs are high. The effect also exists for non-price-based anomalies, but only through the dependence of their profits on momentum. My findings support the view of Lou (2012) and Vayanos and Woolley (2013) that mutual funds’ trading on flows creates feedback that strengthens price-based anomalies, as high-performing funds buy additional shares of high-performing stocks and poorly performing funds sell shares of poorly performing stocks. However, the explanatory power of aggregate mutual fund flows for price-based anomaly returns is only partly attenuated by fund-level variables designed to capture the feedback effect. The flow-induced trading by mutual funds appears to contribute to mispricing for reasons beyond the feedback effect. The second essay examines the extent to which momentum profits depend on the state of credit markets. The state of credit markets does affect momentum, but the results are not consistent with a credit channel effect on momentum. For non-financial firms, the momentum profits are stronger among portfolios formed under favorable credit conditions. For financial firms, credit conditions do not matter to the momentum profits. Price continuations in financial firms are related to whether the firms are performing poorly, but not whether that performance is attributable to credit conditions that are favorable or poor.
Author: Quan Wen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 159
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Author: Michael Shane O'Doherty
Publisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 159
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Book Description
Using a variety of test portfolios, the optimal pool of models consistently outperforms the best individual model on both statistical and economic grounds.
Author: Ching-Chih Lu
Publisher:
ISBN:
Category :
Languages : en
Pages : 328
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Author: Kyungyeon (Rachel) Koh
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ISBN:
Category :
Languages : en
Pages :
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This dissertation aims to shed light on the source of the asset pricing anomalies by investigating behavioral and rational explanations. The first essay, "Asset Efficiency and the Asset Growth Anomaly," examines the source of the asset growth anomaly. I present findings that the anomaly is driven by inefficient firms, which support the behavioral hypothesis that investors on average underreact to some firms' overexpansion. Firms with past records of high asset efficiency relative to their industry peers do not suffer lower stock performance following high growth. The overarching impact of asset efficiency shows that firm skill is highly relevant, for effective corporate strategy should balance growth with capability to maintain and profit from that growth. The next chapter, "Do Financing Costs Matter for the Investment Anomalies?" shows supporting evidence for a shared role of behavioral and rational elements in explaining the anomalies. It comprehensively evaluates whether firms' financing constraints explain the investment anomalies, including the asset growth anomaly, incorporating advanced proxies for financing constraints. The main contribution is to demonstrate that both mispricing and investment-friction channels reinforce each other in explaining the negative investment-return relation. The third chapter, "Style Investing: New Evidence from Mutual Fund Flows," empirically validates the style-investing behavior of mutual fund investors and explores the pricing implication for stocks by utilizing mutual fund flows. Barberis and Shleifer (2003) initially explore the idea of style investing with an assumption that investors choose styles based on the recent past style performance. I find evidence that mutual fund investors allocate to winner styles and withdraw from loser styles based on the recent past style performance, consistently with Barbaris and Shleifer's assumption. Next, I examine the pricing implications of the mutual fund flows by style. The evidence shows the Granger-causality of the style flows and the underlying stock returns in both directions. Neither the rationalists nor the behavioralists have been able to comprehensively explain all of financial market dynamics. This thesis urges the current asset pricing research to stay open-minded to consider various possibilities and viewpoints and be prepared to come up with narratives not confined to a single set of theory.
Author: Serena Frazzoni
Publisher:
ISBN:
Category :
Languages : en
Pages : 77
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Author: John Robert Vogel
Publisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 242
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This dissertation includes three essays of empirical asset pricing. In the first essay, The Value/Growth Anomaly and Hard to Value Firms, I show that combining quality signals (firm fundamentals) and hard to value measures increases the return spread between value and growth portfolios. A portfolio that is long high quality value firms that are hard to value and short low quality growth firms that are hard to value yields a 4-factor alpha of up to 1.41% per month. Second, ex-ante observed quality signals are better at predicting high performance and low performance growth stocks as compared to value stocks. This growth stock mispricing can be explained by extreme quality measures, and enhanced by focusing on hard to value growth firms. In the second essay, Using Maximum Drawdowns to Capture Tail Risk, I, along with my co-author Wesley R. Gray, propose the use of maximum drawdown, the maximum peak to trough loss across a time series of compounded returns, as a simple method to capture an element of risk unnoticed by linear factor models: tail risk. Unlike other tail-risk metrics, maximum drawdown is intuitive and easy-to-calculate. We look at maximum drawdowns to assess tail risks associated with market neutral strategies identified in the academic literature. Our evidence suggests that academic anomalies are not anomalous: all strategies endure large drawdowns at some point in the time series. Many of these losses would trigger margin calls and investor withdrawals, forcing an investor to liquidate. In the third essay, Analyzing Valuation Measures: A Performance Horse Race over the Past 40 Years, I, along with my co-author Wesley R. Gray, show that EBITDA/TEV has historically been the best performing valuation metric and outperforms many investor favorites such as price-to-earnings, free-cash-flow to total enterprise value, and book-to-market. We also explore the investment potential of long-term valuation ratios, which replaces one-year earnings with an average of long-term earnings. In contrast to prior empirical work, we find that long-term ratios add little investment value over standard one-year valuation metrics.
Author: Bruce N. Lehmann
Publisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 52
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Book Description
A review of A Reappraisal of the efficiency of financial markets edited by Rui M.C. Guimaraes, Brian G. Kingsman and Stephen J. Taylor.
