Machine Learning in Finance: From Theory to Practice

Dixon, Matthew F., Halperin, Igor, Bilokon, Paul

Machine Learning in Finance: From Theory to Practice
  • 出版商: Springer
  • 出版日期: 2020-07-02
  • 售價: $3,600
  • 貴賓價: 9.5 折 $3,420
  • 語言: 英文
  • 頁數: 548
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030410676
  • ISBN-13: 9783030410674
  • 相關分類: Machine Learning

    • 立即出貨 (庫存=1)

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商品描述

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 in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

商品描述(中文翻譯)

這本書介紹了金融領域中的機器學習方法。它以統一的方式介紹了機器學習和金融量化領域中的各種統計和計算學科，如金融計量經濟學和離散時間隨機控制，並強調理論和假設檢驗如何影響金融數據建模和決策算法的選擇。隨著計算資源和數據集越來越大的趨勢，機器學習已成為金融行業中重要的技能。本書針對金融計量經濟學、數學金融和應用統計學的高級研究生和學者，以及量化金融領域的量化分析師和數據科學家進行了撰寫。

《從理論到實踐的金融機器學習》分為三個部分，每個部分都涵蓋了理論和應用。第一部分從貝葉斯和頻率論的角度介紹了用於橫斷面數據的監督學習。更高級的材料則重點介紹了神經網絡，包括深度學習，以及高斯過程，在投資管理和衍生品建模方面提供了實例。第二部分介紹了用於時間序列數據的監督學習，這可能是金融中最常用的數據類型，並提供了在交易、隨機波動性和固定收益建模方面的實例。最後，第三部分介紹了強化學習及其在交易、投資和財富管理中的應用。書中提供了Python代碼示例，以支持讀者對方法和應用的理解。該書還包含80多個數學和編程練習題，並提供了教師的解答。作為研究這一新興領域的橋樑，最後一章從研究者的角度介紹了金融機器學習的前沿，並強調統計物理學中許多著名概念可能成為金融機器學習的重要方法論。

作者簡介

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.

作者簡介(中文翻譯)

Paul Bilokon, Ph.D.，是Thalesians Ltd.的首席執行官和創始人。Paul在數學邏輯、領域理論和隨機過濾理論方面做出了貢獻，並與Abbas Edalat合著了一篇享有盛譽的LICS論文。他是英國計算機學會、工程學會和歐洲複雜系統學會的成員。

Matthew Dixon, FRM, Ph.D.，是伊利諾伊理工學院應用數學助理教授，也是斯圖爾特商學院的聯營教授。他在機器學習和量化金融方面發表了20多篇同行評審的論文，並被彭博市場和金融時報引用為金融科技專家。他是《金融機器學習期刊》的副編輯，AIMS動力學和遊戲期刊的副編輯，並且是CFA量化投資小組的顧問委員會成員。

Igor Halperin, Ph.D.，是紐約大學金融工程的研究教授，也是富達投資的人工智能研究聯營。Igor在機器學習、量化金融和理論物理方面發表了50多篇科學文章。在加入金融行業之前，他在以色列理工學院和英屬哥倫比亞大學擔任博士後職位。

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