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Author :
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book Prediction of Stock Returns Using Financial Ratios and Feature Dimensionality Reduction written by and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Wayne Ferson
Publisher : MIT Press
ISBN 13 : 0262039370
Total Pages : 497 pages
Book Rating : 4.2/5 (62 download)
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.
Author : Roy Henriksson
Publisher :
ISBN 13 : 9781021216878
Total Pages : 0 pages
Book Rating : 4.2/5 (168 download)
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:
Author : Shihao Gu
Publisher :
ISBN 13 :
Total Pages : 39 pages
Book Rating : 4.:/5 (14 download)
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.
Author : Melick R. Baranasooriya
Publisher : BPB Publications
ISBN 13 : 9365895871
Total Pages : 450 pages
Book Rating : 4.3/5 (658 download)
Download or read book High-Performance Algorithmic Trading Using AI written by Melick R. Baranasooriya and published by BPB Publications. This book was released on 2024-08-08 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION "High-Performance Algorithmic Trading using AI" is a comprehensive guide designed to empower both beginners and experienced professionals in the finance industry. This book equips you with the knowledge and tools to build sophisticated, high-performance trading systems. It starts with basics like data preprocessing, feature engineering, and ML. Then, it moves to advanced topics, such as strategy development, backtesting, platform integration using Python for financial modeling, and the implementation of AI models on trading platforms. Each chapter is crafted to equip readers with actionable skills, ranging from extracting insights from vast datasets to developing and optimizing trading algorithms using Python's extensive libraries. It includes real-world case studies and advanced techniques like deep learning and reinforcement learning. The book wraps up with future trends, challenges, and opportunities in algorithmic trading. Become a proficient algorithmic trader capable of designing, developing, and deploying profitable trading systems. It not only provides theoretical knowledge but also emphasizes hands-on practice and real-world applications, ensuring you can confidently navigate and leverage AI in your trading strategies. KEY FEATURES ● Master AI and ML techniques to enhance algorithmic trading strategies. ● Hands-on Python tutorials for developing and optimizing trading algorithms. ● Real-world case studies showcasing AI applications in diverse trading scenarios. WHAT YOU WILL LEARN ● Develop AI-powered trading algorithms for enhanced decision-making and profitability. ● Utilize Python tools and libraries for financial modeling and analysis. ● Extract actionable insights from large datasets for informed trading decisions. ● Implement and optimize AI models within popular trading platforms. ● Apply risk management strategies to safeguard and optimize investments. ● Understand emerging technologies like quantum computing and blockchain in finance. WHO THIS BOOK IS FOR This book is for financial professionals, analysts, traders, and tech enthusiasts with a basic understanding of finance and programming. TABLE OF CONTENTS 1. Introduction to Algorithmic Trading and AI 2. AI and Machine Learning Basics for Trading 3. Essential Elements in AI Trading Algorithms 4. Data Processing and Analysis 5. Simulating and Testing Trading Strategies 6. Implementing AI Models with Trading Platforms 7. Getting Prepared for Python Development 8. Leveraging Python for Trading Algorithm Development 9. Real-world Examples and Case Studies 10. Using LLMs for Algorithmic Trading 11. Future Trends, Challenges, and Opportunities
Author : Stefan Nagel
Publisher : Princeton University Press
ISBN 13 : 0691218706
Total Pages : 156 pages
Book Rating : 4.6/5 (912 download)
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.
Author : Ivo Welch
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (129 download)
Download or read book A Note on 'Predicting Returns with Financial Ratios' written by Ivo Welch and published by . This book was released on 2004 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note reinterprets methods that seek to use the aggregate dividend price ratio to predict aggregate stock market returns; specifically, methods which use information about time-varying changes in the dividend-price ratio process to improve the prediction equation. It argues that the empirical evidence is still too weak to suggest practical usefulness of these estimators.
Author : Söhnke M. Bartram
Publisher : CFA Institute Research Foundation
ISBN 13 : 195292703X
Total Pages : 95 pages
Book Rating : 4.9/5 (529 download)
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.
Author : Yacine Ait-Sahalia
Publisher : Elsevier
ISBN 13 : 0080929842
Total Pages : 809 pages
Book Rating : 4.0/5 (89 download)
Download or read book Handbook of Financial Econometrics written by Yacine Ait-Sahalia and published by Elsevier. This book was released on 2009-10-19 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of original articles—8 years in the making—shines a bright light on recent advances in financial econometrics. From a survey of mathematical and statistical tools for understanding nonlinear Markov processes to an exploration of the time-series evolution of the risk-return tradeoff for stock market investment, noted scholars Yacine Aït-Sahalia and Lars Peter Hansen benchmark the current state of knowledge while contributors build a framework for its growth. Whether in the presence of statistical uncertainty or the proven advantages and limitations of value at risk models, readers will discover that they can set few constraints on the value of this long-awaited volume. Presents a broad survey of current research—from local characterizations of the Markov process dynamics to financial market trading activity Contributors include Nobel Laureate Robert Engle and leading econometricians Offers a clarity of method and explanation unavailable in other financial econometrics collections
Author : Stefan Jansen
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216786
Total Pages : 822 pages
Book Rating : 4.8/5 (392 download)
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Author : David Charles Distad
Publisher :
ISBN 13 :
Total Pages : 402 pages
Book Rating : 4.3/5 (129 download)
Download or read book An Inquiry Into the Prediction of Mergers Using Discriminant Analysis on the Financial Ratios of Acquired Firms written by David Charles Distad and published by . This book was released on 1982 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Igor Aizenberg
Publisher : Springer Science & Business Media
ISBN 13 : 1475731159
Total Pages : 274 pages
Book Rating : 4.4/5 (757 download)
Download or read book Multi-Valued and Universal Binary Neurons written by Igor Aizenberg and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-Valued and Universal Binary Neurons deals with two new types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on complex number arithmetic and are hence much more powerful than the typical neurons used in artificial neural networks. Therefore, networks with such neurons exhibit a broad functionality. They can not only realise threshold input/output maps but can also implement any arbitrary Boolean function. Two learning methods are presented whereby these networks can be trained easily. The broad applicability of these networks is proven by several case studies in different fields of application: image processing, edge detection, image enhancement, super resolution, pattern recognition, face recognition, and prediction. The book is hence partitioned into three almost equally sized parts: a mathematical study of the unique features of these new neurons, learning of networks of such neurons, and application of such neural networks. Most of this work was developed by the first two authors over a period of more than 10 years and was only available in the Russian literature. With this book we present the first comprehensive treatment of this important class of neural networks in the open Western literature. Multi-Valued and Universal Binary Neurons is intended for anyone with a scholarly interest in neural network theory, applications and learning. It will also be of interest to researchers and practitioners in the fields of image processing, pattern recognition, control and robotics.
Author : Stephen Satchell
Publisher : Springer
ISBN 13 : 1137554177
Total Pages : 416 pages
Book Rating : 4.1/5 (375 download)
Download or read book Derivatives and Hedge Funds written by Stephen Satchell and published by Springer. This book was released on 2016-05-18 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 20 years hedge funds and derivatives have fluctuated in reputational terms; they have been blamed for the global financial crisis and been praised for the provision of liquidity in troubled times. Both topics are rather under-researched due to a combination of data and secrecy issues. This book is a collection of papers celebrating 20 years of the Journal of Derivatives and Hedge Funds (JDHF). The 18 papers included in this volume represent a small sample of influential papers included during the life of the Journal, representing industry-orientated research in these areas. With a Preface from co-editor of the journal Stephen Satchell, the first part of the collection focuses on hedge funds and the second on markets, prices and products.
Author : Diego Oliva
Publisher : Springer Nature
ISBN 13 : 3030705420
Total Pages : 765 pages
Book Rating : 4.0/5 (37 download)
Download or read book Metaheuristics in Machine Learning: Theory and Applications written by Diego Oliva and published by Springer Nature. This book was released on with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
Author : John Shawe-Taylor
Publisher : Cambridge University Press
ISBN 13 : 9780521813976
Total Pages : 520 pages
Book Rating : 4.8/5 (139 download)
Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Author : Md Rabiul Islam
Publisher : European Alliance for Innovation
ISBN 13 : 1631904264
Total Pages : 1115 pages
Book Rating : 4.6/5 (319 download)
Download or read book ICPDI 2023 written by Md Rabiul Islam and published by European Alliance for Innovation. This book was released on 2023-11-21 with total page 1115 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2nd International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2023) was successfully held on 1-3 September 2023 in Chongqing, China. This conference aimed to bring together researchers, scholars, and practitioners from various fields to exchange ideas and discuss advancements in the areas of public management, digital economy, and internet technology. The conference featured a diverse range of research topics, including but not limited to Public Management, Digital Economy and Internet Technology. The conference fostered a rich and stimulating intellectual environment. The program included keynote speeches by renowned experts in the field, parallel sessions for paper presentations, and panel discussions addressing emerging trends and challenges. The conference proceedings showcased a wide array of research papers, providing valuable insights into the latest theoretical and practical developments in the field of public management, digital economy, and internet technology. Participants had the opportunity to engage in constructive discussions, offer feedback, and establish potential collaborations for future research endeavors. We extend our gratitude to all participants, presenters, organizers, and sponsors for their contributions in making this conference a resounding success. We look forward to the 3rd edition of this conference, where we can further explore the dynamic intersections of public management, digital economy, and internet technology.
Author : Nan Hu
Publisher : International Monetary Fund
ISBN 13 : 1513524089
Total Pages : 37 pages
Book Rating : 4.5/5 (135 download)
Download or read book Completing the Market: Generating Shadow CDS Spreads by Machine Learning written by Nan Hu and published by International Monetary Fund. This book was released on 2019-12-27 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
