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.
This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers ...
This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ...
This book introduces machine learning methods in finance.
Zhang and Zhu (2018) provides a review of recent studies in understanding the representations of neural networks, which are one of the most popular algorithms and are discussed in Chapter 5. Although (deep) neural networks have ...
This book opens the world of q and kdb+ to a wide audience, as it emphasises solutions to problems of practical importance.
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.
This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data.
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 ...
This innovative guide walks you through everything you need to know to fully leverage these revolutionary tools.
The risk that the assets in a defined benefit plan will fall below plan liabilities is an example of a shortfall risk. suppose an investor views any return below a level of RL as unacceptable. roy's safety-first criterion states that ...