This element introduces machine learning (ML) tools that can help asset managers discover economic and financial theories.
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Da: ThriftBooks-Dallas, Dallas, TX, U.S.A.
Paperback. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less. Codice articolo G1108792898I4N00
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Da: Better World Books Ltd, Dunfermline, Regno Unito
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9781108792899
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
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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Da: Rarewaves USA, OSWEGO, IL, U.S.A.
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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Da: PBShop.store UK, Fairford, GLOS, Regno Unito
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo DB-9781108792899
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Da: Speedyhen LLC, Hialeah, FL, U.S.A.
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