| 書目名稱 | Machine Learning in Finance | | 副標題 | From Theory to Pract | | 編輯 | Matthew F. Dixon,Igor Halperin,Paul Bilokon | | 視頻video | http://file.papertrans.cn/621/620671/620671.mp4 | | 概述 | Introduces fundamental concepts in machine learning for canonical modeling and decision frameworks in finance.Presents a unified treatment of machine learning, financial econometrics and discrete time | | 圖書封面 |  | | 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 Bayesianand 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. T | | 出版日期 | Textbook 2020 | | 關鍵詞 | Machine Learning; Financial Mathematics; Financial Econometrics; Neural Networks; Bayesian Neural Networ | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-41068-1 | | isbn_softcover | 978-3-030-41070-4 | | isbn_ebook | 978-3-030-41068-1 | | copyright | Springer Nature Switzerland AG 2020 |
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