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Estimating Latent Asset Pricing Factors
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Book Synopsis Estimating Latent Asset-pricing Factors by : Martin Lettau
Download or read book Estimating Latent Asset-pricing Factors written by Martin Lettau and published by . This book was released on 2018 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with significantly smaller pricing errors.
Book Synopsis Factors that Fit the Time Series and Cross-section of Stock Returns by : Martin Lettau
Download or read book Factors that Fit the Time Series and Cross-section of Stock Returns written by Martin Lettau and published by . This book was released on 2018 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly dominates PCA and finds weak factors with high Sharpe-ratios that PCA cannot detect. Studying a large number of characteristic sorted portfolios we find that five latent factors with economic meaning explain well the cross-section and time-series of returns. We show that out-of-sample the maximum Sharpe-ratio of our five factors is more than twice as large as with PCA with significantly smaller pricing errors. Our factors are based on only a subset of the stock characteristics implying that a significant amount of characteristic information is redundant.
Book Synopsis Large Dimensional Factor Analysis by : Jushan Bai
Download or read book Large Dimensional Factor Analysis written by Jushan Bai and published by Now Publishers Inc. This book was released on 2008 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Book Synopsis Asset Pricing by : B.Philipp Kellerhals
Download or read book Asset Pricing written by B.Philipp Kellerhals and published by Springer Science & Business Media. This book was released on 2012-11-02 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers applications to risky assets traded on the markets for funds, fixed-income products and electricity derivatives. Integrates the latest research and includes a new chapter on financial modeling.
Book Synopsis Macroeconomic Risk and Asset Pricing by : John Ammer
Download or read book Macroeconomic Risk and Asset Pricing written by John Ammer and published by . This book was released on 1993 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis What to Do About a Latent Factor by : Todd Prono
Download or read book What to Do About a Latent Factor written by Todd Prono and published by . This book was released on 2015 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new model misspecification measure for linear asset pricing models is proposed. The origins of this measure are in Shanken (1987) and Kandel and Stambaugh (1985, 1995), where it is argued that the true market return is inherently latent and, as a consequence, only ever partially observed. Tests of asset pricing models that rely on the market return as a risk factor and are based, by necessity, on an observable proxy to this factor are then misspecified. The proposed misspecification measure, which assigns an upper bound to the correlation between the true market return and the observable proxy return used to conduct the test, can be estimated entirely and directly from observable data. This measure is suited both for testing models that include the market return as a pricing factor in a traditional sense (i.e., determining whether the given model does or does not price a collection of risky assets) and ranking those models (i.e., gauging which model performs the best). The measure is used to price portfolios reflecting the size, value, and momentum premiums. While neither the conditional CAPM nor the ICAPM is shown to offer any improvement over the simple CAPM, all three models are shown to perform materially better under the proposed measure, with improvements in model fit of as much as 45%. Also, it is discovered that winner stocks in a momentum portfolio may have higher market betas than loser stocks.
Book Synopsis Asset Pricing with a Factor-arch Covariance Structure by : Robert F. Engle
Download or read book Asset Pricing with a Factor-arch Covariance Structure written by Robert F. Engle and published by . This book was released on 1990 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Autoencoder Asset Pricing Models by : Shihao Gu
Download or read book Autoencoder Asset Pricing Models written by Shihao Gu and published by . This book was released on 2019 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature--autoencoder neural networks--to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models.
Book Synopsis Machine Learning for Factor Investing by : Guillaume Coqueret
Download or read book Machine Learning for Factor Investing written by Guillaume Coqueret and published by CRC Press. This book was released on 2023-08-08 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: a detailed presentation of the key machine learning tools use in finance a large scale coding tutorial with easily reproducible examples realistic applications on a large publicly available dataset all the key ingredients to perform a full portfolio backtest
Download or read book Asset Pricing written by T. Kariya and published by Springer Science & Business Media. This book was released on 2011-06-27 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Main Goals The theory of asset pricing has grown markedly more sophisticated in the last two decades, with the application of powerful mathematical tools such as probability theory, stochastic processes and numerical analysis. The main goal of this book is to provide a systematic exposition, with practical appli cations, of the no-arbitrage theory for asset pricing in financial engineering in the framework of a discrete time approach. The book should also serve well as a textbook on financial asset pricing. It should be accessible to a broad audi ence, in particular to practitioners in financial and related industries, as well as to students in MBA or graduate/advanced undergraduate programs in finance, financial engineering, financial econometrics, or financial information science. The no-arbitrage asset pricing theory is based on the simple and well ac cepted principle that financial asset prices are instantly adjusted at each mo ment in time in order not to allow an arbitrage opportunity. Here an arbitrage opportunity is an opportunity to have a portfolio of value aat an initial time lead to a positive terminal value with probability 1 (equivalently, at no risk), with money neither added nor subtracted from the portfolio in rebalancing dur ing the investment period. It is necessary for a portfolio of valueato include a short-sell position as well as a long-buy position of some assets.
Download or read book Asset Pricing written by Hsien-hsing Liao and published by World Scientific. This book was released on 2003 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real estate finance is a fast-developing area where top quality research is in great demand. In the US, the real estate market is worth about US$4 trillion, and the REITs market about US$200 billion; tens of thousands of real estate professionals are working in this area. The market overseas could be considerably larger, especially in Asia. Given the rapidly growing real estate securities industry, this book fills an important gap in current real estate research and teaching. It is an ideal reference for investment professionals as well as senior MBA and PhD students. Contents: Introduction: Real Estate Analysis in a Dynamic Risk Environment; The Predictability of Returns on Equity REITs and Their Co-Movement with Other Assets; The Predictability of Real Estate Returns and Market Timing; A Time-Varying Risk Analysis of Equity and Real Estate Markets in the US and Japan; Price Reversal, Transaction Costs, and Arbitrage Profits in Real Estate Securities Market; Bank Risk and Real Estate: An Asset Pricing Perspective; Assessing the OC Santa ClausOCO Approach to Asset Allocation: Implications for Commercial Real Estate Investment; The Time-Variation of Risk for Life Insurance Companies; The Return Distributions of Property Shares in Emerging Markets; Conditional Risk Premiums of Asian Real Estate Stocks; Institutional Factors and Real Estate Returns: A Cross-Country Study. Readership: Financial researchers, real estate investors and investment bankers, as well as senior MBA and PhD students."
Book Synopsis Machine Learning in Asset Pricing by : Stefan Nagel
Download or read book Machine Learning in Asset Pricing written by Stefan Nagel and published by Princeton University Press. This book was released on 2021-05-11 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and Data Sciences for Financial Markets by : Agostino Capponi
Download or read book Machine Learning and Data Sciences for Financial Markets written by Agostino Capponi and published by Cambridge University Press. This book was released on 2023-04-30 with total page 742 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Book Synopsis Econometrics with Machine Learning by : Felix Chan
Download or read book Econometrics with Machine Learning written by Felix Chan and published by Springer Nature. This book was released on 2022-09-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Book Synopsis Empirical Asset Pricing by : Wayne Ferson
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Investment Valuation and Asset Pricing by : James W. Kolari
Download or read book Investment Valuation and Asset Pricing written by James W. Kolari and published by Springer Nature. This book was released on 2023-01-01 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is intended to fill a gap in undergraduate finance curriculums by providing an asset pricing text that is accessible for undergraduate finance students. It offers an overview of original works on foundational asset pricing studies that follows their historical publication chronologically throughout the text. Each chapter stays close to the original works of these major authors, including quotations, examples, graphical exhibits, and empirical results. Additionally, it includes statistical concepts and methods as applied to finance. These statistical materials are crucial to learning asset pricing, which often applies statistical tests to evaluate different asset pricing models. It offers practical examples, questions, and problems to help students check their learning and better understand the fundamentals of asset pricing., alongside including PowerPoint slides and an instructor’s manual for professors.
Book Synopsis Regression Based Estimation of Dynamic Asset Pricing Models by : Tobias Adrian
Download or read book Regression Based Estimation of Dynamic Asset Pricing Models written by Tobias Adrian and published by . This book was released on 2015 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose regression based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The estimators explicitly allow for time varying prices of risk, time varying betas and serially dependent pricing factors. Our approach nests the Fama-MacBeth two-pass estimator as a special case. We provide asymptotic multistage standard errors necessary to conduct inference for asset pricing tests. We illustrate our new estimators in an application to the joint pricing of stocks and bonds. The application features strongly time varying, highly significant prices of risk which are found to be quantitatively more important than time varying betas in reducing pricing errors.
