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Advances in Financial Machine Learning
Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual 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 for Asset Managers
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.
Asset Management: Tools And Issues
Long gone are the times when investors could make decisions based on intuition. Modern asset management draws on a wide-range of fields beyond financial theory: economics, financial accounting, econometrics/statistics, management science, operations research (optimization and Monte Carlo simulation), and more recently, data science (Big Data, machine learning, and artificial intelligence). The challenge in writing an institutional asset management book is that when tools from these different fields are applied in an investment strategy or an analytical framework for valuing securities, it is assumed that the reader is familiar with the fundamentals of these fields. Attempting to explain stra...
Machine Learning in Finance
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 t...
Machine Learning and AI in Finance
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. ...
Financial Signal Processing and Machine Learning
The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processi...
Causal Factor Investing
Virtually all journal articles in the factor investing literature make associational claims, instead of causal claims. This Element analyzes the current state of causal confusion and proposes solutions with the potential to transform factor investing into a truly scientific discipline. This title is also available as Open Access on Cambridge Core.
Big Data and Machine Learning in Quantitative Investment
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Machine Learning for Financial Engineering
Preface v 1 On the History of the Growth-Optimal Portfolio M.M. Christensen 1 2 Empirical Log-Optimal Portfolio Selections: A Survey L. Györfi Gy. Ottucsáak A. Urbán 81 3 Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Györfi H. Walk 119 4 Growth-Optimal Portfoho Selection with Short Selling and Leverage M. Horváth A. Urbán 153 5 Nonparametric Sequential Prediction of Stationary Time Series L. Györfi Gy. Ottucsák 179 6 Empirical Pricing American Put Options L. Györfi A. Telcs 227 Index 249.
