Empirical Tests of Asset Pricing Models with Individual Stocks PDF Download

Author: Narasimhan Jegadeesh
Publisher:
ISBN:
Category :
Languages : en
Pages : 54

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Book Description
We develop an instrumental variables methodology to obtain consistent estimates of risk premiums using individual stocks as test assets. Simulation evidence indicates that this methodology yields unbiased estimates of risk premiums and that the associated tests are well specified in small samples. We test a number of recently proposed asset pricing models using this approach. We find that the CAPM market risk, SMB and HML factors risks, investment and ROE factors risks under the production-based asset pricing model and the LCAPM illiquidity-adjusted market risk are not priced.

Author: Narasimhan Jegadeesh
Publisher:
ISBN:
Category :
Languages : en
Pages : 86

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Book Description
To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual assets. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex-post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the CAPM and the liquidity-adjusted CAPM, premiums on risk factors under the Fama-French three- and five-factors models and the Hou, Xue, and Zhang (2015) four-factor model are all insignificant after controlling for asset characteristics.

Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497

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Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Author: Mohammad Sharifzadeh
Publisher: Universal-Publishers
ISBN: 1599423758
Category :
Languages : en
Pages : 180

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Book Description
The problem addressed in this dissertation research was the inability of the single-factor capital asset pricing model (CAPM) to identify relevant risk factors that investors consider in forming their return expectations for investing in individual stocks. Identifying the appropriate risk factors is important for investment decision making and is pertinent to the formation of stocks' prices in the stock market. Therefore, the purpose of this study was to examine theoretical and empirical validity of the CAPM and to develop and test a multifactor model to address and resolve the empirical shortcomings of the single-factor CAPM. To verify the empirical validity of the standard CAPM and of the multifactor model, five hypotheses were developed and tested against historical monthly data for U.S. public companies. Testing the CAPM hypothesis revealed that the explanatory power of the overall stock market rate of return in explaining individual stock's expected rates of return is very weak, suggesting the existence of other risk factors. Testing of the other hypotheses verified that the implied volatility of the overall market as a systematic risk factor and the companies' size and financial leverage as nonsystematic risk factors are important in determining stock's expected returns and investors should consider these factors in their investment decisions. The findings of this research have important implications for social change. The outcome of this study can change the way individual and institutional investors as well as corporations make investment decisions and thus change the equilibrium prices in the stock market. These changes in turn could lead to significant changes in the resource allocation in the economy, in the economy's production capacity and production composition, and in the employment structure of the society.

Author: Soosung Hwang
Publisher:
ISBN:
Category :
Languages : en
Pages : 41

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Book Description
We propose the average F statistic for testing linear asset pricing models. The average pricing error, captured in the the statistic, is of more interest than the ex post maximum pricing error of the multivariate F statistic that is associated with extreme long and short positions and excessively sensitive to small perturbations in the estimates of asset means and covariances. The average F test can be applied to thousands of individual stocks and thus is free from the information loss or the data snooping biases from grouping. This test is robust to ellipticity, and more importantly, our simulation and bootstrapping results show that the power of average F test continues to increase as the number of stocks increases. Empirical tests using individual stocks from 1967 to 2006 demonstrate that the popular four factor model (i.e. Fama-French three factors and momentum) is rejected two sub-periods from from 1967 to 1971 and from 1982 to 1986.

Author: Yan Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
My dissertation contains three chapters. Chapter one proposes a nonparametric method to evaluate the performance of a conditional factor model in explaining the cross section of stock returns. There are two tests: one is based on the individual pricing error of a conditional model and the other is based on the average pricing error. Empirical results show that for valueweighted portfolios, the conditional CAPM explains none of the asset-pricing anomalies, while the conditional Fama-French three-factor model is able to account for the size effect, and it also helps to explain the value effect and the momentum effect. From a statistical point of view, a conditional model always beats a conditional one because it is closer to the true data-generating process. Chapter two proposes a general equilibrium model to study the implications of prospect theory for individual trading, security prices and trading volume. Its main finding is that different components of prospect theory make different predictions. The concavity/convexity of the value function drives a disposition effect, which in turn leads to momentum in the cross-section of stock returns and a positive correlation between returns and volumes. On the other hand, loss aversion predicts exactly the opposite, namely a reversed disposition effect and reversal in the cross-section of stock returns, as well as a negative correlation between returns and volumes. In a calibrated economy, when prospect theory preference parameters are set at the values estimated by the previous studies, our model can generate price momentum of up to 7% on an annual basis. Chapter three studies the role of aggregate dividend volatility in asset prices. In the model, narrow-framing investors are loss averse over fluctuations in the value of their financial wealth. Persistent dividend volatility indicates persistent fluctuation in their financial wealth and makes stocks undesirable. It helps to explain the salient feature of the stock market including the high mean, excess volatility, and predictability of stock returns while maintaining a low and stable risk-free rate. Consistent with the data, stock returns have a low correlation with consumption growth, and Sharpe ratios are time-varying.

Author: Jau-Lian Jeng
Publisher: Springer
ISBN: 3319741926
Category : Business & Economics
Languages : en
Pages : 277

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Book Description
This book analyzes the verification of empirical asset pricing models when returns of securities are projected onto a set of presumed (or observed) factors. Particular emphasis is placed on the verification of essential factors and features for asset returns through model search approaches, in which non-diversifiability and statistical inferences are considered. The discussion reemphasizes the necessity of maintaining a dichotomy between the nondiversifiable pricing kernels and the individual components of stock returns when empirical asset pricing models are of interest. In particular, the model search approach (with this dichotomy emphasized) for empirical model selection of asset pricing is applied to discover the pricing kernels of asset returns.

Author: Philip R. Davies
Publisher:
ISBN: 9780549076537
Category :
Languages : en
Pages : 119

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Book Description
In the first essay I develop a Bayesian approach to test the cross-sectional predictions of the CAPM at the firm level. Using a broad cross-section of NYSE, AMEX, and NASDAQ listed stocks over the period July 1927--June 2005, I find evidence of a robust positive relation between beta and average returns. Fama and French (1993) propose two additional risk factors related to firm size and book-to-market equity. I find no evidence that these additional risk factors help to explain the cross-sectional variation in average returns. These results are consistent with the empirical predictions of the CAPM.

Author: Julian Fischer
Publisher: GRIN Verlag
ISBN: 3346420094
Category : Business & Economics
Languages : en
Pages : 109

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Book Description
Master's Thesis from the year 2021 in the subject Business economics - Investment and Finance, grade: 1,7, University of Hannover (Institut für Finanzwirtschaft und Rohstoffmärkte), language: English, abstract: In this paper, we examine how various modern multifactor models, such as the Carhart factor model, five-factor model and its complement six-factor model by Fama and French, the q-factor model by Hou, Wue and Zhang, and the mispricing factor model by Stambaugh and Yuan perform in the German stock market. It is discernible that, depending on the application model, like factor spanning tests, different sortings, return anomalies, sector- and equity fund investigation, they often provide quite similar explanatory power, while in individual cases sometimes one and sometimes the other model performs better. The underlying factors contribute differently to the explanatory power depending on the time period. Thus, in case of doubt, the six-factor model is preferable, as it is the most versatile model. Since the establishment of the capital asset pricing model as a cornerstone of modern capital market theory in the 1960s, new investigations and studies have been built on this model on an ongoing basis. This continuously leads to extensions and modifications of the asset pricing models since then. These models can be used in various ways, for example to explain the pricing of risky financial assets under restrictive assumptions or to gain important insights into the relationship between expected return and risk of securities. These can be used in various ways, for example to explain the pricing of risky financial assets under restrictive assumptions or to gain important insights into the relationship between expected return and risk of securities. In this paper, we aim to answer the overarching research question of how modern asset pricing models perform for the German stock market. For this purpose, we first discuss the characteristics of the German stock market, followed by the milestones of the development of factor models, their empirical evidence and their factors, as well as internationally known return anomalies. In the subsequent part, five modern asset pricing models are tested in different scenarios of the German stock market, including factor spanning tests, different sortings, anomalies, sectors and in equity funds. For this purpose, various analytical methods are used and performed with the software “Stata”. Finally, the comprehensive results are summarized and concluded.

Author: Stefan Nagel
Publisher: Princeton University Press
ISBN: 0691218706
Category : Business & Economics
Languages : en
Pages : 156

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Book Description
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.