Download or read book Dissecting Characteristics via Machine Learning for Stock Selection written by David Dümig and published by GRIN Verlag. This book was released on 2020-01-31 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Download or read book Your Essential Guide to Quantitative Hedge Fund Investing written by Marat Molyboga and published by CRC Press. This book was released on 2023-07-18 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning and the Cross section of Expected Stock Returns written by Marcial Messmer and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling expected cross-sectional stock returns has a long tradition in asset pricing. My dissertation is motivated by shortcomings of the prevailing and classically used portfolio sorting approach. Consequently, this thesis tackles the task with alternative methodologies. It comprises classical linear models but includes more advanced machine learning algorithms as well. The work contains three chapters. The first chapter uses a linear-shrinkage approach to select relevant firm characteristics (FC), the second leverages a deep-learning architecture to detect non-linear patterns in expected stock returns reviews and the third part compares two established linear models. In short, this thesis covers machine learning based stock picking. The goal of the first chapter is twofold. First, Francesco Audrino and I show, based on Monte Carlo simulations, that the adaptive Lasso methodology is generally suitable for panel specifications. These findings are robust to various distributional assumptions. Second, the empirical task solves the multivariate problem of selecting a set of FC helpful in describing expected stock returns. We find a large number of FC does not survive this shrinkage procedure, however, we document a highly dimensional linear relationship. Chapter 2 loosens the linearity restriction and trains a deep-learning algorithm to identify non-linearities. The tedious task for the researcher is to select appropriate hyper-parameters. I show that random search yields promising results when compared to the linear model based on training data. The portfolio exercise reveals that these benefits materialize on a test data set, a linear model hinges behind the non-linear framework. The final chapter extensively reviews two standard linear estimators. Despite identical objective functions, the two methods exhibit substantial variations with respect to model inference. An investor's perspective shows that these differences lead only.

Download or read book Machine Learning for Asset Management written by Emmanuel Jurczenko and published by John Wiley & Sons. This book was released on 2020-10-06 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Application of Unsupervised Feature Selection Machine Learning and Evolutionary Algorithm in Predicting Stock Returns written by Tamal Chaudhuri and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of stock prices has become an important area of research in the field of financial analytics and has garnered a lot of attention among academicians. Drawing on the literature on application of econometric tools and also machine learning techniques, this paper presents a framework for predicting stock returns using three unsupervised feature selection techniques, four predictive modeling techniques and finally an ensemble combining the four predictive modeling techniques. To design the ensemble, evolutionary algorithm is applied. In order to assess the results of our study, four different performance measures, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Nash-Sutcliffe Efficiency (NSE) and Index of Agreement (IA) have been utilized. Our feature selection results indicate that all explanatory variables are not significant for different classes of companies and also for different time periods. This gives us insight into the fact that, for stock returns prediction, one has to be careful of the predictors to be chosen. Further, results indicate that for all the forecasting methods, namely, random forest, bagging, boosting and support vector regression, forecasting efficiency for large cap and mid-cap firms was better than that of small cap firms. Statistical analysis through Analysis of Variance (ANOVA) suggests that of all four predictive modeling techniques, boosting was the most efficient technique for forecasting the stock returns. We then proceeded to construct an ensemble of the above four methods. In terms of all four measurement metrics, performance of the proposed ensemble was better in both training and testing phase as compared to the efficiency of the individual predictive modeling techniques.

Download or read book Machine Learning for Stock Selection written by Keywan Rasekhschaffe and published by . This book was released on 2019 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships, overfitting poses a major challenge when trying to extract signals from noisy historical data. In this article, we describe some of the basic concepts surrounding machine leaning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.

Download or read book Machine Learning for Stock Selection written by Jun Yan (Ph.D.) and published by . This book was released on 2007 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Machine Learning for Asset Management written by Emmanuel Jurczenko and published by John Wiley & Sons. This book was released on 2020-07-16 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Asset Allocation and Machine Learning written by Steven Wolfseher and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to benchmark a large set of eight contemporary machine learning algorithms in order to identify the best model for the selection of outperforming stocks in Switzerland. The selection process is modelled as a one-year ahead direction-prediction of stocks' excess returns. 65 fundamental, macroeconomic and technical variables are applied to perform the predictions and characterise the analysed 255 publicly listed Swiss companies between 2001 and 2019. The algorithms are compared with regards to their predictive power in the Swiss Performance Index (SPI). Additionally, the models' feature selection is derived and the most significant variables are analysed and discussed. Ultimately, a backtest is performed to verify the profitability of the predictions. The results indicate that ensemble models, namely XGBoost and Random Forest, are the best performing algorithms, selecting outperforming stocks with an accuracy above 80%. Furthermore, the feature selection analysis shows that the most important variables are similar throughout the best performing algorithms, creating a defining effect on the performance. Lastly, when backtested, the best two models yield average excess returns above 30%. This study contributes to extant literature, as it is the first to make such an extensive benchmark in general and specifically on a country level basis in Switzerland.

Download or read book Stock Selection Model Based on Machine Learning with Wisdom of Experts and Crowds written by Xianjiao Wu and published by . This book was released on 2020 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both stock recommendations from sell-side analysts and online user generated content from crowds have great significance in the stock market. We examine and compare different effects of analyst attitude and crowd sentiment on stock prices in this study with data from CSMAR. By estimating a multivariate linear regression model, we find that although the wisdom of both experts and crowds has impact on stock prices, the latter's impact on stock prices prevails. We also adopt LightGBM, a novel machine learning model, to predict stock trend based on empirical results. Portfolio returns of different models also suggest that crowd wisdom is more valuable for creating investment strategy than expert wisdom. And it is necessary to take the wisdom of both experts and crowds into consideration when making investment decision.

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.

Download or read book AI Driven Intelligent Models for Business Excellence written by Samala Nagaraj and published by IGI Global. This book was released on 2022 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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"--

Download or read book Empirical Asset Pricing written by Turan G. Bali and published by John Wiley & Sons. This book was released on 2016-02-26 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: “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.