Empirical Asset Pricing

Empirical Asset Pricing
Author: Wayne Ferson
Publisher: MIT Press
Total Pages: 497
Release: 2019-03-12
Genre: Business & Economics
ISBN: 0262039370

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.

Intelligent Systems and Networks

Intelligent Systems and Networks
Author: Ngoc Le Anh
Publisher: Springer Nature
Total Pages: 700
Release: 2022-07-19
Genre: Technology & Engineering
ISBN: 9811933944

This book presents Proceedings of the International Conference on Intelligent Systems and Networks (ICISN 2022), held at Hanoi in Vietnam. It includes peer reviewed high quality articles on Intelligent System and Networks. It brings together professionals and researchers in the area and presents a platform for exchange of ideas and to foster future collaboration. The topics covered in this book include- Foundations of Computer Science; Computational Intelligence Language and speech processing; Software Engineering Software development methods; Wireless Communications Signal Processing for Communications; Electronics track IoT and Sensor Systems Embedded Systems; etc. ​

Machine Learning in Complex Networks

Machine Learning in Complex Networks
Author: Thiago Christiano Silva
Publisher: Springer
Total Pages: 345
Release: 2016-01-28
Genre: Computers
ISBN: 3319172905

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

Quantitative Portfolio Management

Quantitative Portfolio Management
Author: Michael Isichenko
Publisher: John Wiley & Sons
Total Pages: 311
Release: 2021-08-31
Genre: Business & Economics
ISBN: 1119821320

Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.

Machine Learning Bookcamp

Machine Learning Bookcamp
Author: Alexey Grigorev
Publisher: Simon and Schuster
Total Pages: 470
Release: 2021-11-23
Genre: Computers
ISBN: 1617296813

The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.

Predictions, Nonlinearities and Portfolio Choice

Predictions, Nonlinearities and Portfolio Choice
Author: Friedrich Christian Kruse
Publisher: BoD – Books on Demand
Total Pages: 222
Release: 2012
Genre: Business & Economics
ISBN: 3844101853

Finance researchers and asset management practitioners put a lot of effort into the question of optimal asset allocation. With this respect, a lot of research has been conducted on portfolio decision making as well as quantitative modeling and prediction models. This study brings together three fields of research, which are usually analyzed in an isolated manner in the literature: - Predictability of asset returns and their covariance matrix - Optimal portfolio decision making - Nonlinear modeling, performed by artificial neural networks, and their impact on predictions as well as optimal portfolio construction Including predictability in asset allocation is the focus of this work and it pays special attention to issues related to nonlinearities. The contribution of this study to the portfolio choice literature is twofold. First, motivated by the evidence of linear predictability, the impact of nonlinear predictions on portfolio performances is analyzed. Predictions are empirically performed for an investor who invests in equities (represented by the DAX index), bonds (represented by the REXP index) and a risk-free rate. Second, a solution to the dynamic programming problem for intertemporal portfolio choice is presented. The method is based on functional approximations of the investor's value function with artificial neural networks. The method is easily capable of handling multiple state variables. Hence, the effect of adding predictive parameters to the state space is the focus of analysis as well as the impacts of estimation biases and the view of a Bayesian investor on intertemporal portfolio choice. One important empirical result shows that residual correlation among state variables have an impact on intertemporal portfolio decision making.

Intelligent Systems

Intelligent Systems
Author: André Britto
Publisher: Springer Nature
Total Pages: 564
Release: 2021-11-27
Genre: Computers
ISBN: 3030917029

The two-volume set LNAI 13073 and 13074 constitutes the proceedings of the 10th Brazilian Conference on Intelligent Systems, BRACIS 2021, held in São Paolo, Brazil, in November-December 2021. The total of 77 papers presented in these two volumes was carefully reviewed and selected from 192 submissions.The contributions are organized in the following topical sections: Part I: Agent and Multi-Agent Systems, Planning and Reinforcement Learning; Evolutionary Computation, Metaheuristics, Constrains and Search, Combinatorial and Numerical Optimization, Knowledge Representation, Logic and Fuzzy Systems; Machine Learning and Data Mining. Part II: Multidisciplinary Artificial and Computational Intelligence and Applications; Neural Networks, Deep Learning and Computer Vision; Text Mining and Natural Language Processing. Due to the COVID-2019 pandemic, BRACIS 2021 was held as a virtual event.

Computerworld

Computerworld
Author:
Publisher:
Total Pages: 96
Release: 1990-01-29
Genre:
ISBN:

For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network.

Machine Learning for Asset Management

Machine Learning for Asset Management
Author: Emmanuel Jurczenko
Publisher: John Wiley & Sons
Total Pages: 460
Release: 2020-10-06
Genre: Business & Economics
ISBN: 1786305445

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.