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Matthew F. Dixon

Machine Learning in Finance

From Theory to Practice

Buch

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, mathemat… Mehr

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Produktdetails


Weitere Autoren: Bilokon, Paul / Halperin, Igor
  • ISBN: 978-3-030-41067-4
  • EAN: 9783030410674
  • Produktnummer: 34352567
  • Verlag: Springer International Publishing
  • Sprache: Englisch
  • Erscheinungsjahr: 2020
  • Seitenangabe: 576 S.
  • Masse: H24.1 cm x B16.0 cm x D3.6 cm 1'021 g
  • Auflage: 1st ed. 2020
  • Abbildungen: HC runder Rücken kaschiert
  • Gewicht: 1021

Über den Autor


Paul Bilokon, Ph.D., is CEO and Founder of Thalesians Ltd. Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. He is a member of the British Computer Society, the Institution of Engineering and the European Complex Systems Society.Matthew Dixon, FRM, Ph.D., is an Assistant Professor of Applied Math at the Illinois Institute of Technology and an Affiliate of the Stuart School of Business. He has published over 20 peer reviewed publications on machine learning and quant finance and has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert. He is Deputy Editor of the Journal of Machine Learning in Finance, Associate Editor of the AIMS Journal on Dynamics and Games, and is a member of the Advisory Board of the CFA Quantitative Investing Group.Igor Halperin, Ph.D., is a Research Professor in Financial Engineering at NYU, and an AI Research associate at Fidelity Investments. Igor has published more than 50 scientific articles in machine learning, quantitative finance and theoretic physics. Prior to joining the financial industry, he held postdoctoral positions in theoretical physics at the Technion and the University of British Columbia.

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