Machine learning for asset managers:
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. Th...
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2020
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Schriftenreihe: | Cambridge elements. Elements in quantitative finance, 2631-8571
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Schlagworte: | |
Online-Zugang: | BSB01 UBG01 URL des Erstveröffentlichers |
Zusammenfassung: | 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 |
Beschreibung: | 1 Online-Ressource (141 Seiten) |
ISBN: | 9781108883658 |
DOI: | 10.1017/9781108883658 |
Internformat
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Datensatz im Suchindex
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author | López de Prado, Marcos M. 1975- |
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discipline | Wirtschaftswissenschaften |
discipline_str_mv | Wirtschaftswissenschaften |
doi_str_mv | 10.1017/9781108883658 |
format | Electronic eBook |
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institution | BVB |
isbn | 9781108883658 |
language | English |
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spelling | López de Prado, Marcos M. 1975- Verfasser (DE-588)1078853738 aut Machine learning for asset managers Marcos M. López de Prado Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore Cambridge University Press 2020 1 Online-Ressource (141 Seiten) txt rdacontent c rdamedia cr rdacarrier Cambridge elements. Elements in quantitative finance, 2631-8571 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 Portfoliomanagement (DE-588)4115601-8 gnd rswk-swf Vermögensverwaltung (DE-588)4063089-4 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Kapitalanlage (DE-588)4073213-7 gnd rswk-swf Asset-liability management ; Data processing Machine learning Künstliche Intelligenz (DE-588)4033447-8 s Vermögensverwaltung (DE-588)4063089-4 s Kapitalanlage (DE-588)4073213-7 s Portfoliomanagement (DE-588)4115601-8 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-108-79289-9 https://doi.org/10.1017/9781108883658 Verlag URL des Erstveröffentlichers Volltext |
spellingShingle | López de Prado, Marcos M. 1975- Machine learning for asset managers Portfoliomanagement (DE-588)4115601-8 gnd Vermögensverwaltung (DE-588)4063089-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Kapitalanlage (DE-588)4073213-7 gnd |
subject_GND | (DE-588)4115601-8 (DE-588)4063089-4 (DE-588)4033447-8 (DE-588)4073213-7 |
title | Machine learning for asset managers |
title_auth | Machine learning for asset managers |
title_exact_search | Machine learning for asset managers |
title_exact_search_txtP | Machine learning for asset managers |
title_full | Machine learning for asset managers Marcos M. López de Prado |
title_fullStr | Machine learning for asset managers Marcos M. López de Prado |
title_full_unstemmed | Machine learning for asset managers Marcos M. López de Prado |
title_short | Machine learning for asset managers |
title_sort | machine learning for asset managers |
topic | Portfoliomanagement (DE-588)4115601-8 gnd Vermögensverwaltung (DE-588)4063089-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Kapitalanlage (DE-588)4073213-7 gnd |
topic_facet | Portfoliomanagement Vermögensverwaltung Künstliche Intelligenz Kapitalanlage |
url | https://doi.org/10.1017/9781108883658 |
work_keys_str_mv | AT lopezdepradomarcosm machinelearningforassetmanagers |