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Author: David Dümig
Publisher: GRIN Verlag
ISBN: 3346106551
Category : Business & Economics
Languages : en
Pages : 103
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Book Description
Academic Paper from the year 2019 in the subject Business economics - Investment and Finance, , language: English, abstract: We conduct a comparative analysis of methods in the machine learning repertoire, including penalized linear models, generalized linear models, boosted regression trees, random forests, and neural networks, that investors can deploy to forecast the cross-section of stock returns. Gaining more widespread use in economics, machine learning algorithms have demonstrated the ability to reveal complex, nonlinear patterns that are difficult or largely impossible to detect with conventional statistical methods and are often more robust to the effects of multi-collinearity among predictors. We provide new evidence that machine learning techniques can improve the economic value of cross-sectional return forecasts. The implications of machine learning for quantitative finance are becoming both increasingly apparent and controversial. There is a growing discussion over whether machine learning tools can and should be applied to predict stock returns with greater precision. Broadly speaking, models that can be used to explain the returns of individual stocks draw on stock and firm characteristics, such as the market price of financial instruments and companies' accounting data. These characteristics can also be used to predict expected returns out-of-sample.
Author: David Dümig
Publisher: GRIN Verlag
ISBN: 3346106551
Category : Business & Economics
Languages : en
Pages : 103
Get Book Here
Book Description
Academic Paper from the year 2019 in the subject Business economics - Investment and Finance, , language: English, abstract: We conduct a comparative analysis of methods in the machine learning repertoire, including penalized linear models, generalized linear models, boosted regression trees, random forests, and neural networks, that investors can deploy to forecast the cross-section of stock returns. Gaining more widespread use in economics, machine learning algorithms have demonstrated the ability to reveal complex, nonlinear patterns that are difficult or largely impossible to detect with conventional statistical methods and are often more robust to the effects of multi-collinearity among predictors. We provide new evidence that machine learning techniques can improve the economic value of cross-sectional return forecasts. The implications of machine learning for quantitative finance are becoming both increasingly apparent and controversial. There is a growing discussion over whether machine learning tools can and should be applied to predict stock returns with greater precision. Broadly speaking, models that can be used to explain the returns of individual stocks draw on stock and firm characteristics, such as the market price of financial instruments and companies' accounting data. These characteristics can also be used to predict expected returns out-of-sample.
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: Marat Molyboga
Publisher: CRC Press
ISBN: 100090461X
Category : Mathematics
Languages : en
Pages : 317
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Book Description
Your Essential Guide to Quantitative Hedge Fund Investing provides a conceptual framework for understanding effective hedge fund investment strategies. The book offers a mathematically rigorous exploration of different topics, framed in an easy to digest set of examples and analogies, including stories from some legendary hedge fund investors. Readers will be guided from the historical to the cutting edge, while building a framework of understanding that encompasses it all. Features Filled with novel examples and analogies from within and beyond the world of finance Suitable for practitioners and graduate-level students with a passion for understanding the complexities that lie behind the raw mechanics of quantitative hedge fund investment A unique insight from an author with experience of both the practical and academic spheres.
Author: Emmanuel Jurczenko
Publisher: John Wiley & Sons
ISBN: 1786305445
Category : Business & Economics
Languages : en
Pages : 460
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Book Description
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Author: Emmanuel Jurczenko
Publisher: John Wiley & Sons
ISBN: 1119751179
Category : Business & Economics
Languages : en
Pages : 460
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Book Description
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
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: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Škrinjari?, Tihana
Publisher: IGI Global
ISBN: 1799850846
Category : Business & Economics
Languages : en
Pages : 432
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Book Description
In today’s financial market, portfolio and risk management are facing an array of challenges. This is due to increasing levels of knowledge and data that are being made available that have caused a multitude of different investment models to be explored and implemented. Professionals and researchers in this field are in need of up-to-date research that analyzes these contemporary models of practice and keeps pace with the advancements being made within financial risk modelling and portfolio control. Recent Applications of Financial Risk Modelling and Portfolio Management is a pivotal reference source that provides vital research on the use of modern data analysis as well as quantitative methods for developing successful portfolio and risk management techniques. While highlighting topics such as credit scoring, investment strategies, and budgeting, this publication explores diverse models for achieving investment goals as well as improving upon traditional financial modelling methods. This book is ideally designed for researchers, financial analysts, executives, practitioners, policymakers, academicians, and students seeking current research on contemporary risk management strategies in the financial sector.
Author: Turan G. Bali
Publisher: John Wiley & Sons
ISBN: 1118589475
Category : Business & Economics
Languages : en
Pages : 512
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Book Description
“Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.” Eugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences “The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray’s clear and careful guide to these issues provides a firm foundation for future discoveries.” John Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University “Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.” Kenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College “This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.” Lubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago Empirical Asset Pricing: The Cross Section of Stock Returns is a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. Empirical Asset Pricing: The Cross Section of Stock Returns also includes: Discussions on the driving forces behind the patterns observed in the stock market An extensive set of results that serve as a reference for practitioners and academics alike Numerous references to both contemporary and foundational research articles Empirical Asset Pricing: The Cross Section of Stock Returns is an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics. Turan G. Bali, PhD, is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley. Robert F. Engle, PhD, is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics. Scott Murray, PhD, is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize.
Author: Samala Nagaraj
Publisher: IGI Global
ISBN: 1668442485
Category : Computers
Languages : en
Pages : 293
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Book Description
"As digital technology is taking the world in a revolutionary way and business related aspects are getting smarter this book is a potential research source on the Artificial Intelligence-based Business Applications and Intelligence"--
