Machine Learning in Finance: From Theory to Practice
暫譯: 金融中的機器學習：從理論到實踐

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。它統一了機器學習與量化金融中各種統計和計算學科的處理，例如金融計量經濟學和離散時間隨機控制，強調理論和假設檢驗如何影響金融數據建模和決策過程中算法的選擇。隨著計算資源的增加和數據集的擴大，機器學習已成為金融行業中一項重要的技能。本書是為金融計量經濟學、數學金融和應用統計的高級研究生和學者，以及量化分析師和數據科學家而寫的，特別是在量化金融領域。

《金融中的機器學習：從理論到實踐》分為三個部分，每個部分涵蓋理論和應用。第一部分從貝葉斯和頻率主義的角度介紹了針對橫斷面數據的監督學習。更高級的內容則強調神經網絡，包括深度學習，以及高斯過程，並提供了在投資管理和衍生品建模中的示例。第二部分介紹了針對時間序列數據的監督學習，這可以說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中的示例。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。本書提供了 Python 代碼示例，以支持讀者對方法論和應用的理解。書中還包含了超過 80 道數學和編程練習，並提供了可供教師使用的解答。作為通往這一新興領域研究的橋樑，最後一章從研究者的角度介紹了金融中機器學習的前沿，強調許多著名的統計物理概念可能會成為金融中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)

保羅·比洛肯（Paul Bilokon），博士，是Thalesians Ltd.的首席執行官及創始人。保羅在數學邏輯、領域理論和隨機過濾理論方面做出了貢獻，並與阿巴斯·艾達特（Abbas Edalat）共同發表了一篇享有盛譽的LICS論文。他是英國計算機學會、工程學會及歐洲複雜系統學會的成員。

馬修·迪克森（Matthew Dixon），FRM，博士，是伊利諾伊理工學院應用數學的助理教授，並且是斯圖爾特商學院的附屬研究員。他在機器學習和量化金融方面發表了超過20篇經過同行評審的論文，並在《彭博市場》（Bloomberg Markets）和《金融時報》（Financial Times）中被引用為金融科技（fintech）領域的人工智慧專家。他是《金融機器學習期刊》（Journal of Machine Learning in Finance）的副編輯、《AIMS動態與遊戲期刊》（AIMS Journal on Dynamics and Games）的副編輯，並且是CFA量化投資小組的顧問委員會成員。

伊戈爾·哈爾佩林（Igor Halperin），博士，是紐約大學金融工程的研究教授，並且是富達投資（Fidelity Investments）的人工智慧研究助理。伊戈爾在機器學習、量化金融和理論物理方面發表了超過50篇科學文章。在進入金融行業之前，他曾在以色列理工學院和不列顛哥倫比亞大學擔任理論物理的博士後研究員。

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