Articoli correlati a Machine Learning for Asset Managers
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This element introduces machine learning (ML) tools that can help asset managers discover economic and financial theories.
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Machine Learning for Asset Managers
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MACHINE LEARNING FOR ASSET MANAGERS
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Machine Learning for Asset Managers
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Cambridge University Pr. Apr 2020, 2020
ISBN 10: 1108792898
ISBN 13: 9781108792899
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Taschenbuch. Condizione: 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. Codice articolo 9781108792899
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Machine Learning for Asset Managers
Editore:
Cambridge University Press, GB, 2020
ISBN 10: 1108792898
ISBN 13: 9781108792899
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Da: Rarewaves.com UK, London, Regno Unito
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Paperback. Condizione: 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. Codice articolo LU-9781108792899
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Machine Learning for Asset Managers
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Condizione: New. 2020. Paperback. . . . . . Codice articolo V9781108792899
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Machine Learning for Asset Managers
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo DB-9781108792899
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Machine Learning for Asset Managers
Editore:
Cambridge University Press, GB, 2020
ISBN 10: 1108792898
ISBN 13: 9781108792899
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Paperback
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
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Paperback. Condizione: 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. Codice articolo LU-9781108792899
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Machine Learning for Asset Managers
Print on DemandDa: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 75 pages. 9.00x6.00x0.25 inches. In Stock. This item is printed on demand. Codice articolo __1108792898
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Machine Learning for Asset Managers (Elements in Quantitative Finance)
Da: California Books, Miami, FL, U.S.A.
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Condizione: New. Codice articolo I-9781108792899
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Machine Learning for Asset Managers
Editore:
Cambridge University Press, GB, 2020
ISBN 10: 1108792898
ISBN 13: 9781108792899
Nuovo
Paperback
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: 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. Codice articolo LU-9781108792899
Quantità: Più di 20 disponibili