大葉大學圖書館 |

Machine learning for asset managers[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	332.10681
書名/作者:	Machine learning for asset managers/ Marcos M. López de Prado.
作者:	López de Prado, Marcos Mailoc.
出版者:	Cambridge : : Cambridge University Press,, 2020.
面頁冊數:	141 p. : : ill., digital ;; 24 cm.
附註:	Title from publisher's bibliographic system (viewed on 08 Apr 2020).
標題:	Asset-liability management - Data processing.
標題:	Machine learning.
ISBN:	9781108883658
ISBN:	9781108792899
摘要、提要註:	Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.
電子資源:	https://doi.org/10.1017/9781108883658

Machine learning for asset managers[electronic resource] /
López de Prado, Marcos Mailoc.

Machine learning for asset managers[electronic resource] /Marcos M. López de Prado. - Cambridge :Cambridge University Press,2020. - 141 p. :ill., digital ;24 cm. - Cambridge elements. Elements in quantitative finance. - Cambridge elements.Elements in quantitative finance..

Title from publisher's bibliographic system (viewed on 08 Apr 2020).

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

ISBN: 9781108883658Subjects--Topical Terms:

748129
Asset-liability management
--Data processing.

LC Class. No.: HG1615.25 / .L66 2020

Dewey Class. No.: 332.10681

Machine learning for asset managers[electronic resource] /
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