Empirical Tests of Asset Pricing Models Based on Analysts' Forecasts PDF Download

Author: Ruoling Shen
Publisher:
ISBN:
Category :
Languages : en
Pages : 26

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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: 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: 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: 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.

Author: Nataliya Polkovnichenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 214

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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: 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: Youfei Xiao
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
This dissertation is comprised of two studies on analyst forecasts. The first study provides empirical evidence about the objective function underlying analysts' choice of forecasts. Assumptions about sell-side analysts' objective function are critical to empirical researchers' understanding of their incentives and resulting behavior. In contrast to approaches used in previous papers which rely exclusively on statistical properties of forecasts, I compare theoretical models with alternate objective functions based on their ability to explain observed forecasts. A linear loss objective function which incorporates the effect future analysts' actions on analysts' deviation from peer forecasts is best rationalized by the data. I find that assumptions about the objective function have a substantial impact on the conclusions from empirical tests about analysts' incentives and behavior. The second study provides empirical estimates of uncertainty and disagreement about future earnings that underly analyst forecast dispersion. A parsimonious model which assumes that analysts' payoffs are jointly determined by forecast error and deviation from consensus reproduces many of the descriptive facts observed about forecast dispersion in the data. The strategic behavior that arises from the model distorts both the levels of forecast dispersion and the sensitivity of the measure with respect to cross-sectional variation in uncertainty. The estimated parameters perform better at predicting forecast dispersion out-of-sample than approaches based solely on regressions that use firm characteristics. Counterfactual simulations indicate that analysts' strategic incentives, together with the sequential forecast setting, plays a first-order role in determining forecast dispersion relative to the firm's information environment. The model-implied estimates of earnings uncertainty exhibit a substantially less negative association with future returns relative to the association generated by forecast dispersion. This finding partially reconciles the findings from previous studies with theories about the asset pricing implications of uncertainty and disagreement.