Share

Machine Learning for Asset Managers

2020-04-22 Business & Economics
Download Machine Learning for Asset Managers PDF Online Free

Author :
Release : 2020-04-22
Genre : Business & Economics
Kind : eBook
Book Rating : 721/5 ( reviews)

GET EBOOK


Book Synopsis Machine Learning for Asset Managers by : Marcos M. López de Prado

Download or read book Machine Learning for Asset Managers written by Marcos M. López de Prado. This book was released on 2020-04-22. Available in PDF, EPUB and Kindle. Book excerpt: Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Machine Learning for Asset Management

2020-10-06 Business & Economics
Download Machine Learning for Asset Management PDF Online Free

Author :
Release : 2020-10-06
Genre : Business & Economics
Kind : eBook
Book Rating : 445/5 ( reviews)

GET EBOOK


Book Synopsis Machine Learning for Asset Management by : Emmanuel Jurczenko

Download or read book Machine Learning for Asset Management written by Emmanuel Jurczenko. This book was released on 2020-10-06. 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.

Artificial Intelligence in Asset Management

2020-08-28 Business & Economics
Download Artificial Intelligence in Asset Management PDF Online Free

Author :
Release : 2020-08-28
Genre : Business & Economics
Kind : eBook
Book Rating : 03X/5 ( reviews)

GET EBOOK


Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram

Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram. This book was released on 2020-08-28. 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.

Advances in Financial Machine Learning

2018-01-23 Business & Economics
Download Advances in Financial Machine Learning PDF Online Free

Author :
Release : 2018-01-23
Genre : Business & Economics
Kind : eBook
Book Rating : 119/5 ( reviews)

GET EBOOK


Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado. This book was released on 2018-01-23. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Machine Learning in Finance

2020-07-01 Business & Economics
Download Machine Learning in Finance PDF Online Free

Author :
Release : 2020-07-01
Genre : Business & Economics
Kind : eBook
Book Rating : 684/5 ( reviews)

GET EBOOK


Book Synopsis Machine Learning in Finance by : Matthew F. Dixon

Download or read book Machine Learning in Finance written by Matthew F. Dixon. This book was released on 2020-07-01. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

You may also like...