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|a 1178643411
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|a 332.60285416
|2 23
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|a UAMI
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| 245 |
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|a Machine Learning for Asset Management
|b New Developments and Financial Applications /
|c edited by Emmanuel Jurczenko.
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| 264 |
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1 |
|a London :
|b Wiley-ISTE,
|c 2020.
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| 300 |
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|a 1 online resource
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| 505 |
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|a Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- 1. Time-series and Cross-sectional Stock Return Forecasting: New Machine Learning Methods -- 1.1. Introduction -- 1.2. Time-series return forecasts -- 1.2.1. Predictive regression -- 1.2.2. Forecast combination -- 1.2.3. Elastic net -- 1.2.4. Combination elastic net -- 1.3. Empirical application -- 1.3.1. Data -- 1.3.2. Forecasts -- 1.3.3. Statistical gains -- 1.3.4. Economic gains -- 1.4. Cross-sectional return forecasts -- 1.5. Conclusion -- 1.6. Acknowledgements -- 1.7. References
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| 505 |
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|a 2. In Search of Return Predictability: Application of Machine Learning Algorithms in Tactical Allocation -- 2.1. Introduction -- 2.2. Empirical investigation -- 2.2.1. The data -- 2.2.2. Tactical asset allocation strategy -- 2.2.3. Implementation -- 2.2.4. Benchmarks -- 2.3. A review of machine learning algorithms for prediction of market direction -- 2.3.1. K-nearest neighbors -- 2.3.2. Generalized linear model -- 2.3.3. Elastic net regression -- 2.3.4. Linear discriminant analysis -- 2.3.5. Support vector machines with radial kernel -- 2.3.6. C5.0 -- 2.3.7. Random forests
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| 505 |
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|a 2.3.8. Multilayer perceptron -- 2.3.9. Model averaging -- 2.3.10. Repeated k-fold cross validation -- 2.4. Evaluation criteria -- 2.4.1. Statistical performance -- 2.4.2. Financial performance -- 2.4.3. Significant features -- 2.5. Results and findings -- 2.5.1. Descriptive statistics of the data -- 2.5.2. Statistical performance -- 2.5.3. Financial performance -- 2.5.4. The best performer, benchmark and model average -- 2.5.5. LIME -- 2.6. Conclusion -- 2.7. Acknowledgments -- 2.8. References -- 3. Sparse Predictive Regressions: Statistical Performance and Economic Significance
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|a 3.1. Introduction -- 3.2. Related literature -- 3.3. Data: portfolios and predictors -- 3.4. Econometric framework -- 3.4.1. Shrinkage priors -- 3.4.2. Forecast evaluations -- 3.5. Predicting asset returns: empirical results -- 3.5.1. Statistical performance -- 3.5.2. Economic significance -- 3.6. Discussion on the dynamics of sparsity -- 3.7. Conclusion -- 3.8. Appendix -- 3.9. Posterior simulation -- 3.9.1. Ridge regression -- 3.9.2. Lasso and group-lasso -- 3.9.3. Elastic net -- 3.9.4. Horseshoe and the group horseshoe -- 3.10. References
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|a 4. The Artificial Intelligence Approach to Picking Stocks -- 4.1. Introduction -- 4.2. Literature review -- 4.3. Data -- 4.3.1. Equity factors -- 4.3.2. Data cleaning -- 4.3.3. Features used for training and prediction -- 4.4. Model specification and calibration -- 4.4.1. Models -- 4.4.2. Model calibration -- 4.5. Predicting US stock returns -- 4.5.1. Information coefficients -- 4.5.2. Long-short strategy -- 4.5.3. Returns correlation with Alpha model -- 4.5.4. Active returns by basket -- 4.5.5. Calibrated hyperparameters and model complexity -- 4.5.6. Variable importance
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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|a Investments
|x Data processing.
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| 650 |
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6 |
|a Investissements
|x Informatique.
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|
7 |
|a Investments
|x Data processing
|2 fast
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| 700 |
1 |
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|a Jurczenko, Emmanuel,
|e editor.
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| 758 |
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|i has work:
|a Machine learning for asset management (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFYCcJXrF3kgkHdyTWFjbq
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
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|i Print version:
|a Jurczenko, Emmanuel
|t Machine Learning for Asset Management : New Developments and Financial Applications
|d Newark : John Wiley & Sons, Incorporated,c2020
|z 9781786305442
|
| 856 |
4 |
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|z Texto completo
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