Articles liés à Machine Learning for Asset Managers
Synopsis
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.
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MACHINE LEARNING FOR ASSET MANAGERS
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Cambridge University Press, 2020
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Machine Learning for Asset Managers (Elements in Quantitative Finance)
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Machine Learning for Asset Managers
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Cambridge University Pr. Apr 2020, 2020
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Taschenbuch. Etat : Neu. Neuware -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. N° de réf. du vendeur 9781108792899
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Machine Learning for Asset Managers
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Machine Learning for Asset Managers
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Cambridge University Press, GB, 2020
ISBN 10 : 1108792898
ISBN 13 : 9781108792899
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Paperback. Etat : New. 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. N° de réf. du vendeur LU-9781108792899
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Machine Learning for Asset Managers (Elements in Quantitative Finance)
Edité par
Cambridge University Press, 2020
ISBN 10 : 1108792898
ISBN 13 : 9781108792899
Neuf
Couverture souple
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Machine Learning for Asset Managers
Edité par
Cambridge University Press, GB, 2020
ISBN 10 : 1108792898
ISBN 13 : 9781108792899
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Paperback
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
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Paperback. Etat : New. 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. N° de réf. du vendeur LU-9781108792899
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Machine Learning for Asset Managers
impression à la demandeVendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 75 pages. 9.00x6.00x0.25 inches. In Stock. This item is printed on demand. N° de réf. du vendeur __1108792898
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Machine Learning for Asset Managers (Elements in Quantitative Finance)
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Cambridge University Press 2020-03-31, 2020
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Paperback
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Machine Learning for Asset Managers
Edité par
Cambridge University Pr. Apr 2020, 2020
ISBN 10 : 1108792898
ISBN 13 : 9781108792899
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. Neuware -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. 139 pp. Englisch. N° de réf. du vendeur 9781108792899
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