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Joint-Learning, Belief-Covariations, and Asset Prices

Joint-Learning, Belief-Covariations, and Asset Prices
Author: James Yae
Publisher:
Total Pages:
Release: 2018
Genre:
ISBN:

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Information and learning environments shape the dynamics of our beliefs that determine asset prices. When an agent jointly learns about consumption and dividend, her beliefs on them inter-temporally co-vary with each other, decoupled from their true underlying relationship. Such information-driven belief-co-variation can create endogenous exposures of financial assets to learning-induced macroeconomic risks in general equilibrium models. Therefore, even a financial asset without any fundamental connection to consumption can bear a huge risk premium that increases with jointness of signals. Also, both overall and relative precision of signals can affect the equity premium non-monotonically following an inverted U-shape curve, which can give us a hint on how on-going innovations in big-data-driven investments will affect asset prices.


Learning, Dynamics of Beliefs, and Asset Pricing

Learning, Dynamics of Beliefs, and Asset Pricing
Author: Tobias Adrian
Publisher:
Total Pages: 139
Release: 2003
Genre:
ISBN:

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(Cont.) Asset price movements in the country affected by contagion are not justified by its own fundamentals. Contagion leads to an increase in the covariance of international financial markets during periods of financial crisis. Two particular events are tested: the stock market crash of 1929 and the Latin American debt crises of 1931. In both events the hypothesis that the crises spread contagiously is rejected with one exception: the French Stock Market.


Empirical Asset Pricing

Empirical Asset Pricing
Author: Wayne Ferson
Publisher: MIT Press
Total Pages: 497
Release: 2019-03-12
Genre: Business & Economics
ISBN: 0262039370

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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.


Asset Prices with Rational Beliefs

Asset Prices with Rational Beliefs
Author: Mordecai Kurz
Publisher:
Total Pages:
Release: 1998
Genre:
ISBN:

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This paper introduces the concept of Rational Belief Equilibrium (RBE) as a basis for a new theory of asset pricing. Rational Beliefs are probability beliefs about future economic variables which cannot be contradicted by the data generated by the economy. RBE is an equilibrium in which the diverse beliefs of all the agents induce an equilibrium stochastic process of prices and quantities and these beliefs are, in general, wrong in the sense that they are different from the true probability of the equilibrium process. These beliefs are, however, Rational. Consequently, in an RBE agents use the wrong forecasting functions and their forecasting mistakes play a crucial role in the analysis. First, we show that these mistakes are the reason why stock returns are explainable in retrospect and forecastable whenever the environment remains unchanged over a long enough time interval for agents to learn the forecasting function. Second, the aggregation of these mistakes generates Endogenous Uncertainty: it is that component of the variability of stock prices and returns which is endogenously induced by the beliefs and actions of the agents rather than by the standard exogenous state variables. The paper develops some basic propositions and empirical implications of the theory of RBE. Based on the historical background of the post world war II era, we formulate an econometric model of stock returns which allows non-stationarity in the form of changing environments (quot;regimesquot;). A sequence of econometric hypotheses are then formulated as implications of the theory of RBE and tested utilizing data on common stock returns in the post war period. Apart from confirming the validity of our theory, the empirical analysis shows that (i) common stock returns are forecastable within each environment but it takes time for agents to learn and approximate the forecasting functions. For some agents the time is too short so that it is too late to profit from such learning; (ii) the equilibrium forecasting functions change from one environment to the other in an unforecastable manner so that learning the parameters of one environment does not improve the ability to forecast in the subsequent environments. (iii) more than 2/3 of the variability of stock returns is due to endogenous uncertainty rather than exogenous causes. The paper analyzes one example of a gross market overvaluation which was induced in the 1960's by an aggregation of agent's Mistakes.


Asset Pricing

Asset Pricing
Author: John H. Cochrane
Publisher: Princeton University Press
Total Pages: 560
Release: 2009-04-11
Genre: Business & Economics
ISBN: 1400829135

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Winner of the prestigious Paul A. Samuelson Award for scholarly writing on lifelong financial security, John Cochrane's Asset Pricing now appears in a revised edition that unifies and brings the science of asset pricing up to date for advanced students and professionals. Cochrane traces the pricing of all assets back to a single idea--price equals expected discounted payoff--that captures the macro-economic risks underlying each security's value. By using a single, stochastic discount factor rather than a separate set of tricks for each asset class, Cochrane builds a unified account of modern asset pricing. He presents applications to stocks, bonds, and options. Each model--consumption based, CAPM, multifactor, term structure, and option pricing--is derived as a different specification of the discounted factor. The discount factor framework also leads to a state-space geometry for mean-variance frontiers and asset pricing models. It puts payoffs in different states of nature on the axes rather than mean and variance of return, leading to a new and conveniently linear geometrical representation of asset pricing ideas. Cochrane approaches empirical work with the Generalized Method of Moments, which studies sample average prices and discounted payoffs to determine whether price does equal expected discounted payoff. He translates between the discount factor, GMM, and state-space language and the beta, mean-variance, and regression language common in empirical work and earlier theory. The book also includes a review of recent empirical work on return predictability, value and other puzzles in the cross section, and equity premium puzzles and their resolution. Written to be a summary for academics and professionals as well as a textbook, this book condenses and advances recent scholarship in financial economics.


Machine Learning in Asset Pricing

Machine Learning in Asset Pricing
Author: Stefan Nagel
Publisher: Princeton University Press
Total Pages: 156
Release: 2021-05-11
Genre: Business & Economics
ISBN: 0691218706

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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.