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Deep Models For Empirical Asset Pricing Risk Premia Forecast And Their Interpretability
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Book Synopsis Deep Models for Empirical Asset Pricing (risk-premia Forecast) and Their Interpretability by : Manish Singh (S.M.)
Download or read book Deep Models for Empirical Asset Pricing (risk-premia Forecast) and Their Interpretability written by Manish Singh (S.M.) and published by . This book was released on 2020 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk premia measurement is an essential problem in Asset Pricing. It is estimation of how much an asset will outperform risk-free assets. Problems like noisy and non-stationarity of returns makes risk-premia estimation using Machine Learning (ML) challenging. In this work, we develop ML models that solve the associated problems with risk-premia measurement by decoupling risk-premia prediction into two independent tasks and by using ideas from Deep Learning literature that enables deep neural networks training. The models are tested robustly using different metrics where we observe that our model outperforms existing standard ML models. One another problem with ML models is their black-box nature. We also interpret the deep neural networks using local approximation based techniques that make the predictions explainable.
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 Deep Sequence Modeling by : Lin William Cong
Download or read book Deep Sequence Modeling written by Lin William Cong and published by . This book was released on 2020 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a promising path with its data-driven approach and superior performance. In this paper, we first overview the development of deep sequence models, introduce their applications in asset pricing, and discuss their advantages and limitations. We then perform a comparative analysis of these methods using data on U.S. equities. We demonstrate how sequence modeling benefits investors in general through incorporating complex historical path dependence, and that Long- and Short-term Memory (LSTM) based models tend to have the best out-of-sample performance.
Book Synopsis Empirical Asset Pricing Models by : Jau-Lian Jeng
Download or read book Empirical Asset Pricing Models written by Jau-Lian Jeng and published by Springer. This book was released on 2018-03-19 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 Essays in Empirical Asset Pricing by : Eric Marius Pondi Endengle
Download or read book Essays in Empirical Asset Pricing written by Eric Marius Pondi Endengle and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis has three chapters in which I develop tools for comparisons and dynamic analysis of linear asset pricing models. In the first chapter, I introduce the notion of dynamically useless factors: factors that may be useless (uncorrelated with the assets returns) at some periods of time, while relevant at other periods of time. This notion bridges the literature on classical empirical asset pricing and the literature on useless factors, where both assume that the relevance of a factor remains constant through time. In this new framework, I propose a modified Fama-Macbeth procedure to estimate the time-varying risk premia from conditional linear asset pricing models. At each date, my estimator consistently estimates the conditional risk premium for every useful factor and is robust to the presence of the dynamically useless ones. I apply this methodology to the Fama-French five-factor model and find that, with the exception of the market, all the factors of this model are dynamically useless, although they remain useful 90 percent of the time. In the second chapter, I infer the time-varying parameters of a potentially misspecified stochastic discount factor (SDF) model. I extend the model of Gospodinov et al. (2014) to the framework of conditional SDF models, as the coefficients and the covariances are allowed to vary over time. The proposed misspecification-robust inference is able to eliminate the negative effects of potential useless factors, while maintaining the relevance of the useful ones. Empirically, I analyze the dynamical relevance of each factor in seven common asset pricing models from 1963 to 2016. The Fama-French's three-factor model (FF3) and five factor model (FF5) have been the overall best SDFs in the last 50 years. However, since 2000, the best SDF is CARH (FF3 + momentum factor), followed by FF5 as the second best. Apart from traded factors, the results bring a nuance on non-traded factors. We analyze the relevance, for linear pricing, of a human capital model inspired by Lettau & Ludvigson (2001) and Gospodinov et al. (2014). The third chapter proposes a method for ranking Fama-French linear factor models according to investors' preference for higher-order moments. I show that adding a new Fama- French factor to a prior Fama-French model systematically leads to a better model, only when the preference for higher-order moments is moderate (in absolute value). When the preference for higher-order moments is important or extreme, the four-factor model of Carhart (1997) has a better pricing ability than all the Fama-French models. An analysis of models with non-traded factors confirms the relevance, for linear pricing, of the human capital model analyzed in the second chapter. However, I show that this relevance is effective only for investors with null or very low preferences for higher-order moments.
Book Synopsis Financial Markets and the Real Economy by : John H. Cochrane
Download or read book Financial Markets and the Real Economy written by John H. Cochrane and published by Now Publishers Inc. This book was released on 2005 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Markets and the Real Economy reviews the current academic literature on the macroeconomics of finance.
Book Synopsis Empirical Tests of Asset Pricing Models with Individual Assets by : Narasimhan Jegadeesh
Download or read book Empirical Tests of Asset Pricing Models with Individual Assets written by Narasimhan Jegadeesh and published by . This book was released on 2018 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Book Synopsis Factor risk premia in asset pricing models by : Demetra Kazanga
Download or read book Factor risk premia in asset pricing models written by Demetra Kazanga and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on the Specification Testing for Dynamic Asset Pricing Models by : Jaeho Yun
Download or read book Essays on the Specification Testing for Dynamic Asset Pricing Models written by Jaeho Yun and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays on the subjects of specification testing on dynamic asset pricing models. In the first essay (with Yongmiao Hong), "A Simulation Test for Continuous-Time Models," we propose a simulation method to implement Hong and Li's (2005) transition density-based test for continuous-time models. The idea is to simulate a sequence of dynamic probability integral transforms, which is the key ingredient of Hong and Li's (2005) test. The proposed procedure is generally applicable whether or not the transition density of a continuous-time model has a closed form and is simple and computationally inexpensive. A Monte Carlo study shows that the proposed simulation test has very similar sizes and powers to the original Hong and Li's (2005) test. Furthermore, the performance of the simulation test is robust to the choice of the number of simulation iterations and the number of discretization steps between adjacent observations. In the second essay (with Yongmiao Hong), "A Specification Test for Stock Return Models," we propose a simulation-based specification testing method applicable to stochastic volatility models, based on Hong and Li (2005) and Johannes et al. (2008). We approximate a dynamic probability integral transform in Hong and Li' s (2005) density forecasting test, via the particle filters proposed by Johannes et al. (2008). With the proposed testing method, we conduct a comprehensive empirical study on some popular stock return models, such as the GARCH and stochastic volatility models, using the S&P 500 index returns. Our empirical analysis shows that all models are misspecified in terms of density forecast. Among models considered, however, the stochastic volatility models perform relatively well in both in- and out-of-sample. We also find that modeling the leverage effect provides a substantial improvement in the log stochastic volatility models. Our value-at-risk performance analysis results also support stochastic volatility models rather than GARCH models. In the third essay (with Yongmiao Hong), "Option Pricing and Density Forecast Performances of the Affine Jump Diffusion Models: the Role of Time-Varying Jump Risk Premia," we investigate out-of-sample option pricing and density forecast performances for the affine jump diffusion (AJD) models, using the S&P 500 stock index and the associated option contracts. In particular, we examine the role of time-varying jump risk premia in the AJD specifications. For comparison purposes, nonlinear asymmetric GARCH models are also considered. To evaluate density forecasting performances, we extend Hong and Li's (2005) specification testing method to be applicable to the famous AJD class of models, whether or not model-implied spot volatilities are available. For either case, we develop (i) the Fourier inversion of the closed-form conditional characteristic function and (ii) the Monte Carlo integration based on the particle filters proposed by Johannes et al. (2008). Our empirical analysis shows strong evidence in favor of time-varying jump risk premia in pricing cross-sectional options over time. However, for density forecasting performances, we could not find an AJD specification that successfully reconcile the dynamics implied by both time-series and options data.
Book Synopsis The Market Risk Premium and Empirical Tests of Asset Pricing Models with Higher Moments by : R. Stephen Sears
Download or read book The Market Risk Premium and Empirical Tests of Asset Pricing Models with Higher Moments written by R. Stephen Sears and published by . This book was released on 1986 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Dynamic Asset Pricing by : Kenneth J. Singleton
Download or read book Empirical Dynamic Asset Pricing written by Kenneth J. Singleton and published by Princeton University Press. This book was released on 2009-12-13 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the leading experts in the field, this book focuses on the interplay between model specification, data collection, and econometric testing of dynamic asset pricing models. The first several chapters provide an in-depth treatment of the econometric methods used in analyzing financial time-series models. The remainder explores the goodness-of-fit of preference-based and no-arbitrage models of equity returns and the term structure of interest rates; equity and fixed-income derivatives prices; and the prices of defaultable securities. Singleton addresses the restrictions on the joint distributions of asset returns and other economic variables implied by dynamic asset pricing models, as well as the interplay between model formulation and the choice of econometric estimation strategy. For each pricing problem, he provides a comprehensive overview of the empirical evidence on goodness-of-fit, with tables and graphs that facilitate critical assessment of the current state of the relevant literatures. As an added feature, Singleton includes throughout the book interesting tidbits of new research. These range from empirical results (not reported elsewhere, or updated from Singleton's previous papers) to new observations about model specification and new econometric methods for testing models. Clear and comprehensive, the book will appeal to researchers at financial institutions as well as advanced students of economics and finance, mathematics, and science.
Book Synopsis Essentials of Excel VBA, Python, and R by : John Lee
Download or read book Essentials of Excel VBA, Python, and R written by John Lee and published by Springer Nature. This book was released on 2023-03-23 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.
Book Synopsis A Test for a Multi-risk Premia International Asset Pricing Model by : Carl B. McGowan
Download or read book A Test for a Multi-risk Premia International Asset Pricing Model written by Carl B. McGowan and published by . This book was released on 1987 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asset Pricing written by John H. Cochrane and published by Princeton University Press. This book was released on 2009-04-11 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado
Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
