Download or read book Predicting Stock Returns with Firm Characteristics by Machine Learning Techniques written by Shihao Gu and published by . This book was released on 2017 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose multiple advanced learning methods to deal with the "curse of dimensionality" challenge in the cross-sectional stock returns. Our purpose is to predict the one-month-ahead stock returns by the rm characteristics which are so-called "anomalies". Compared with the traditional methods like portfolio sorting and Fama Factor models, we focus on using all existing machine learning methods to do the prediction rather than the explanation. To alleviate the concern of excessive data mining, we use several regularization penalties that can lead to a sparse and robust model. Our method can identify the return predictors with incremental pricing information and learn the interaction effects by applying to a hierarchical structure. Our best method can achieve much higher out of sample R2 and portfolio Sharp Ratios than traditional linear regression method.

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 DIY Financial Advisor written by Wesley R. Gray and published by John Wiley & Sons. This book was released on 2015-08-31 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth DIY Financial Advisor is a synopsis of our research findings developed while serving as a consultant and asset manager for family offices. By way of background, a family office is a company, or group of people, who manage the wealth a family has gained over generations. The term 'family office' has an element of cachet, and even mystique, because it is usually associated with the mega-wealthy. However, practically speaking, virtually any family that manages its investments—independent of the size of the investment pool—could be considered a family office. The difference is mainly semantic. DIY Financial Advisor outlines a step-by-step process through which investors can take control of their hard-earned wealth and manage their own family office. Our research indicates that what matters in investing are minimizing psychology traps and managing fees and taxes. These simple concepts apply to all families, not just the ultra-wealthy. But can—or should—we be managing our own wealth? Our natural inclination is to succumb to the challenge of portfolio management and let an 'expert' deal with the problem. For a variety of reasons we discuss in this book, we should resist the gut reaction to hire experts. We suggest that investors maintain direct control, or at least a thorough understanding, of how their hard-earned wealth is managed. Our book is meant to be an educational journey that slowly builds confidence in one's own ability to manage a portfolio. We end our book with a potential solution that could be applicable to a wide-variety of investors, from the ultra-high net worth to middle class individuals, all of whom are focused on similar goals of preserving and growing their capital over time. DIY Financial Advisor is a unique resource. This book is the only comprehensive guide to implementing simple quantitative models that can beat the experts. And it comes at the perfect time, as the investment industry is undergoing a significant shift due in part to the use of automated investment strategies that do not require a financial advisor's involvement. DIY Financial Advisor is an essential text that guides you in making your money work for you—not for someone else!

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 Predicting Stock Returns Using Industry Relative Firm Characteristics written by Clifford S. Asness and published by . This book was released on 2000 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Better proxies for the information about future returns contained in firm characteristics such as size, book-to-market equity, cash flow-to-price, percent change in employees, and various past return measures are obtained by breaking these explanatory variables into two industry-related components. The components represent (1) the difference between firms' own characteristics and the average characteristics of their industries (within-industry variables), and (2) the average characteristics of firms' industries (across-industry variables). Each variable is reliably priced within-industry and measuring the variables within-industry produces more precise estimates than measuring the variables in their more common form. Contrary to Moskowitz and Grinblatt [1999], we find that within-industry momentum (i.e., the firm's past return less the industry average return) has predictive power for the firm's stock return beyond that captured by across-industry momentum. We also document a significant short-term (one-month) industry momentum effect which remains strongly significant when we restrict the sample to only the most liquid firms.

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 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 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 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 On Market Timing and Investment Performance Part II Statistical Procedures for Evaluating Forecasting Skills written by Roy Henriksson and published by . This book was released on 2023-07-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Prediction of Stock Performance with Machine Learning Techniques written by David Loeliger and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Selecting high-performing stocks among a vast number of available securities is still one of the investor's prime concerns. While the number of different approaches to find these thriving stocks is enormous, many methods are based on fundamental financial indicators predicting future firm performance. With the continuing advances in computational sciences, machine learning methods are used to analyse the fundamental financial ratios for stock performance prediction. Given the large number of financial performance measures, it is evident that not all of them are equally useful to predict stock performances. Large differences in business models between different industries have the effect that financial ratios cannot be used to the same extent for performance predictions in every industry. Research in the field of performance prediction with machine learning methods on financial indicators currently focuses solely on entire markets, neglecting the different fundamental ratio characteristics between the industry sectors. Current research focuses only on prediction performance and therefore neglects the interpretation of the significance of the underlying financial indicators. This study therefore aims to employ a machine learning method for stock performance prediction not only on the overall market, but specifically for every major industrial sector. Additionally, the importance of the financial ratios used for the analysis is discussed with respect to concepts of classical financial analysis. This research shows the possibility to beat the stock market performance for specific years under analysis, applying a machine learning method that includes fundamental financial ratios. The industry breakdown shows that there are large differences in prediction ability between the different industries ranging from a rather predictable materials sector to an unpredictable information technology sector. Focusing on the importance of the financ.

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 168 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 Machine Learning for Factor Investing R Version written by Guillaume Coqueret and published by CRC Press. This book was released on 2020-08-31 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

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 Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-04-10 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.

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.