There is a great deal of interest in machine learning within the finance industry. Financial professionals are looking for ways to use machine learning to improve their decision making, and to gain an edge over their competition. There are a number of books on the topic of machine learning in finance, which can be a helpful resource for those looking to learn more about the topic. In this blog post, we will discuss some of the most popular books on machine learning in finance, and provide a summary of each one. We hope that this information will be helpful for readers who are interested in learning more about this topic.
Machine Learning in Finance: From Theory to Practice
By Matthew F. Dixon, Igor Halperin, Paul Bilokon
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 for Asset Managers
By Marcos M. López de Prado
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 in Business: An Introduction to the World of Data Science
By John C Hull
This book is for business executives and students who want to learn about the tools used in machine learning. In creating the third edition, John Hull has continued to improve his material. He has added new case studies and new material on the applications of neural networks. The book explains the most popular algorithms clearly and succinctly without using calculus or matrix/vector algebra. The focus is on business applications. There are many illustrative examples throughout the book. These include assessing the risk of a country for international investment, predicting the value of real estate, classifying retail loans as acceptable or unacceptable, understanding the behavior of interest rates, using neural networks to understand volatility surface movements, and using reinforcement learning for optimal trade execution.
Machine Learning: An Applied Mathematics Introduction
By Paul Wilmott
A fully self-contained introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the most important techniques.
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
By Stefan Jansen
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.
Conclusion
As machine learning becomes more accessible to people outside of the tech industry, finance professionals are integrating it into their workflows. For example, AI is being used in fintech companies for fraud detection and market predictions; financial planning software like Quicken (a product that helps manage personal finances) has integrated natural language processing capabilities that allow users to ask questions about investments or other topics using everyday words rather than complicated acronyms, and business intelligence firms use predictive algorithms to identify patterns in company data which can help them make decisions on where they should invest capital. We have a number of articles related to this topic on our website if you want to learn more.
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