東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning for asset managers

López de Prado, Marcos Mailoc.

FindBook

Google Book

Amazon

博客來

Machine learning for asset managers

紀錄類型:	書目-電子資源 : Monograph/item
正題名/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. -
電子資源:	https://doi.org/10.1017/9781108883658
ISBN:	9781108883658

Machine learning for asset managers
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:

3615857
Asset-liability management
--Data processing.

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

Dewey Class. No.: 332.10681

Machine learning for asset managers
LDR:01942nmm a2200253 a 4500 001 2308927
003 UkCbUP
005 20200513000846.0
006 m d
007 cr nn 008maaau
008 230530s2020 enk o 1 0 eng d
020 $a 9781108883658 $q (electronic bk.)
020 $a 9781108792899 $q (paperback)
035 $a CR9781108883658
040 $a UkCbUP $b eng $c UkCbUP $d GP
050 4 $a HG1615.25 $b .L66 2020
082 0 4 $a 332.10681 $2 23
090 $a HG1615.25 $b .L864 2020
100 1 $a López de Prado, Marcos Mailoc. $3 3615855
245 1 0 $a Machine learning for asset managers $h [electronic resource] / $c Marcos M. López de Prado.
260 $a Cambridge : $b Cambridge University Press, $c 2020.
300 $a 141 p. : $b ill., digital ; $c 24 cm.
490 1 $a Cambridge elements. Elements in quantitative finance
500 $a Title from publisher's bibliographic system (viewed on 08 Apr 2020).
520 $a 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.
650 0 $a Asset-liability management $x Data processing. $3 3615857
650 0 $a Machine learning. $3 533906
830 0 $a Cambridge elements. $p Elements in quantitative finance. $3 3615856
856 4 0 $u https://doi.org/10.1017/9781108883658