Big Data and Machine Learning in Quantitative Investment

• Main Author: Guida, Tony 1979- (Author)
• Format: Book
• Language: English
• Published: West Sussex John Wiley & Sons, Ltd 2019
• Series: Wiley Finance Series
• Subjects: Investments > Study and teaching; Machine learning.; Big data
• Online Access: Click Here to View Status and Holdings.
• Physical Description: vi, 285 pages illustrations 25 cm
• Bibliography: Includes bibliographical references (pages 276-277) and index
• ISBN: 9781119522195

"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"--
"Sales handles: ACTIONABLE CONTENT: it is the only book on the subject written by practitioners for practitioners, focusing on the "why" and "how" of using machine learning and big data in finance. It is not a book on mathematical demonstration or coding HIGH-CALIBER AUTHOR TEAM with wide networks within the Quant community. Great opportunities for promotion and possibly buybacks HOT TOPIC: machine learning and artificial intelligence are of huge interest to finance institutions looking to gain an edge Marketing Decription: Each of the authors is well known and respected in the Quant Finance field; each has a wide professional network, and speaks regularly at major Quant conferences around the world. They are also members of Quant finance organisations such as Opalesque, London Quant group, Inquire, CFA Financial Journal, EDHEC Risk, QuantCon and Re-Work Deep learning"--