9781009702423 - machine learning for asset managers di marcos m. lopez de prado (19 risultati)
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Books From California, Simi Valley, CA, U.S.A.Books From California
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Lingua: Inglese
Editore: Cambridge University Press, 2026
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Lingua: Inglese
Editore: Cambridge University Press, 2026
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Condizione: New.
- Altre immagini
Lingua: Inglese
Editore: Cambridge University Press, GB, 2026
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Hardback. 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 p…urpose 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.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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hardcover. Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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Condizione: New. 2026. hardcover. . . . . .
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Lingua: Inglese
Editore: Cambridge University Press, 2026
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Condizione: New. 2026. hardcover. . . . . . Books ship from the US and Ireland.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 152 pages. 6.00x0.38x9.00 inches. In Stock.
- Altre immagini
Lingua: Inglese
Editore: Cambridge University Press Jan 2026, 2026
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 81,17
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Buch. 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.
- Altre immagini
Lingua: Inglese
Editore: Cambridge University Press, GB, 2026
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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EUR 78,31
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Hardback. 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 p…urpose 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.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 72,24
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Hardcover. Condizione: Brand New. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2026
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 86,19
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Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 111,33
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Condizione: New. Print on Demand.
Lingua: Inglese
Editore: Cambridge University Press, 2026
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 110,55
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Condizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2026
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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EUR 79,91
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Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2026
- Rilegato
- Print on Demand
Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 116,72
EUR 32,50 spedizioneSpedito da Australia a U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
