Machine Learning for Factor Investing: R Version: R Version (Chapman and Hall/CRC Financial Mathematics Series)
$62.12
Description
Book Synopsis: Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.
The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models.
All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience.
If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.
Details
Are you ready to take your investment strategies to the next level? Machine Learning for Factor Investing: R Version is the ultimate guide for those looking to harness the power of machine learning in the field of quantitative finance and algorithmic trading. With ML becoming increasingly popular among hedge funds and asset managers, now is the perfect time to join the bandwagon and unlock the potential of alpha signal generation and stocks selection.
But we understand that machine learning can be a complex and daunting subject, especially for non-specialists. That's where Machine Learning for Factor Investing: R Version comes in. This comprehensive book bridges the gap between technicality and accessibility, providing you with a step-by-step tour of modern ML-based investment strategies. From economic rationales to rigorous portfolio back-testing, every aspect is covered.
One of the highlights of this book is its focus on firm characteristics. Learn how to leverage common supervised learning algorithms like tree models and neural networks in the context of style investing. Dive deeper into more advanced techniques such as autoencoder asset returns, Bayesian additive trees, and causal models. And the best part? Every concept is explained using self-contained R code samples and snippets that you can easily apply to your own projects.
Worried about access to the dataset? Don't be! We've got you covered. Not only does the book provide a large public dataset with over 90 predictors, but all the material and content is also available online. This means you can reproduce and enhance the examples at your convenience, without any hassle.
Whether you're a seasoned pro or just starting to dip your toes into the world of quantitative finance, Machine Learning for Factor Investing: R Version is the perfect resource to help you level up your knowledge and expertise. Get your copy today and unlock the secrets of machine learning for factor investing.
Click here to get your hands on Machine Learning for Factor Investing: R Version now!
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