Machine Learning for Asset Managers (Elements in Quantitative Finance) - Softcover
Book 1 of 3: Elements in Quantitative Finance
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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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ISBN 13: 9781108792899
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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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Machine Learning for Asset Managers (Elements in Quantitative Finance)
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ISBN 13: 9781108792899
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Machine Learning for Asset Managers
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Paperback. Condition: 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. Seller Inventory # LU-9781108792899
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Machine Learning for Asset Managers
Published by
Cambridge University Press, GB, 2020
ISBN 10: 1108792898
ISBN 13: 9781108792899
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Paperback. Condition: 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. Seller Inventory # LU-9781108792899
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MACHINE LEARNING FOR ASSET MANAGERS
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