000			02303nam a2200349 i 4500
001			CR9781108883658
003			UkCbUP
005			20240912192154.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			191031s2020||||enk o ||1 0|eng|d
020			_a9781108883658 (ebook)
020			_z9781108792899 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050		4	_aHG1615.25 _b.L66 2020
082	0	4	_a332.10681 _223
100	1		_aLópez de Prado, Marcos Mailoc, _eauthor.
245	1	0	_aMachine learning for asset managers / _cMarcos M. López de Prado.
264		1	_aCambridge : _bCambridge University Press, _c2020.
300			_a1 online resource (141 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aCambridge elements. Elements in quantitative finance, 2631-8571
500			_aTitle from publisher's bibliographic system (viewed on 08 Apr 2020).
520			_aSuccessful 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 _xData processing.
650		0	_aMachine learning.
776	0	8	_iPrint version: _z9781108792899
856	4	0	_uhttps://doi.org/10.1017/9781108883658
942			_2ddc _cEB
999			_c9388 _d9388