Machine Learning for Asset Management New Developments and Financial Applications /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Jurczenko, Emmanuel (Editor )
Formato: eBook
Idioma:Inglés
Publicado: London : Wiley-ISTE, 2020.
Temas:
Investments > Data processing.
Investissements > Informatique.
Investments > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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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