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Big Data Science in Finance
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Title Page
Copyright
Preface
REFERENCE
Chapter 1: Why Big Data?
Introduction
Appendix 1.A Coding Big Data in Python
Reference
Notes
Chapter 2: Neural Networks in Finance
Introduction
Neural Network Construction Methodology
The Architecture of Neural Networks
Choosing the Activation Function
Construction and Training of Neural Networks
Model Selection via Dropout
Overfitting
Adding Complexity
Big Data in Machine Learning
Coding a Simple Neural Network for One Instrument from Daily Data
Defining Target Outputs
Testing Performance
Adding Activation Levels
Convergence
Choosing Input Variables
Conclusion
Appendix 2.A Building a Neural Network in Python
References
Chapter 3: Supervised Learning
Introduction
Supervised Learning
Conclusion
Appendix 3.A Python for Supervised Models
References
Chapter 4: Modeling Human Behavior with Semi-Supervised Learning
Introduction
Performance Evaluation via Cross-Validation
Generative Models
Other SSL Models and Enhancements
Conclusion
Appendix 4.A Python for Semi-Supervised Models
References
Chapter 5: Letting the Data Speak with Unsupervised Learning
Introduction
Dimensionality Reduction in Finance
Conclusion
Appendix 5.A PCA and SVD in Python
References
Chapter 6: Big Data Factor Models
Why PCA and SVD Deliver Optimal Factorization
Eigenportfolios
Using Factors to Predict Returns
Factor Discovery
Instrumented PCA
The Three-Pass Model
Risk-Premium PCA
Nonlinear Factorization
Correlation-Based Factors
Hierarchical PCA (HPCA)
Disadvantages of PCA and SVD
Conclusion
Appendix 6.A Python for Big Data Factor Models
References
Note
Chapter 7: Data as a Signal versus Noise
Introduction
Random Data Shows in Eigenvalue Distribution
Application: What's in the Data Bag?
The Marčenko-Pastur Theorem
Spike Model: Which Value to Pick on the “Elbow”?
Dealing with Highly Correlated Data
Deconstructing the Mona Lisa
What's in the Data Bag?
Applications
The Karhunen-Loève Transform
Data Imputation
Missing Eigenvalues
The Tracy-Widom Distribution
Identifying (and Replacing) Missing Values in Streaming Data (the Johnson-Lindenstrauss Lemma)
Conclusion
Appendix 7 Finding the Optimal Number of Eigenvectors in Python
References
Chapter 8: Applications: Unsupervised Learning in Option Pricing and Stochastic Modeling
Introduction
Application 1: Unsupervised Learning in Options Pricing
Application 2: Optimizing Markov Chains with the Perron-Frobenius Theorem
Conclusion
Appendix 8.A Determining the Percentage of Variation Explained by the Top Principal Components in Python
References
Note
Chapter 9: Data Clustering
Introduction
Clustering Methodology
Clustering Financial Data
Empirical Results
Conclusion
Appendix 9.A Clustering with Python
References
Conclusion
Index
End User License Agreement
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Big Data Science in Finance
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Copyright
Big Data Science in Finance
By
Irene Aldridge
Marco Avellaneda
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