Artificial Intelligence for Financial Markets: The Polymodel Approach

Foreword
Preface
Contents
Chapter 1: Introduction
	1.1 Financial Market Predictions: A Concise Literature Review
	1.2 A Simple Model of Portfolio Returns
	1.3 Plan of the Book
	References
Chapter 2: Polymodel Theory: An Overview
	2.1 Introduction
	2.2 Mathematical Formulation
	2.3 Epistemological Foundations
		2.3.1 A Statistical Perspectivism
		2.3.2 A Phenomenological Approach
	2.4 Comparison of Polymodels to Multivariate Models
		2.4.1 Reducing Overfitting
		2.4.2 Increasing Precision
		2.4.3 Increasing Robustness
	2.5 Considerations Raised by Polymodels
		2.5.1 Aggregation of Predictions
		2.5.2 Number of Variables Per Elementary Model
	2.6 Conclusions
	References
Chapter 3: Estimation Method: The Linear Non-Linear Mixed Model
	3.1 Introduction
	3.2 Presentation of the LNLM Model
		3.2.1 Definition
		3.2.2 Fitting Procedure
	3.3 Evaluation Methodology
	3.4 Results
		3.4.1 For 126 Observations (Tables 3.1 and 3.2)
		3.4.2 For 252 Observations (Tables 3.3 and 3.4)
		3.4.3 For 756 Observations (Tables 3.5 and 3.6)
		3.4.4 For 1,260 Observations (Tables 3.7 and 3.8)
		3.4.5 Computation Time (Table 3.9)
		3.4.6 Interpretations
	3.5 Conclusions
	References
Chapter 4: Predictions of Market Returns
	4.1 Introduction
	4.2 Data
	4.3 Systemic Risk Indicator
		4.3.1 Model Estimation
		4.3.2 Systemic Risk Indicator Definition
		4.3.3 Primary Analysis
		4.3.4 Roots of the Predictive Power
	4.4 Trading Strategy
		4.4.1 Methodology
		4.4.2 Results
	4.5 Robustness Tests
		4.5.1 Sensitivity to the Noise Filter
		4.5.2 Sensitivity to Future Returns Windows
		4.5.3 Sensitivity to Half-life
		4.5.4 Sensitivity to Rolling Window Length
		4.5.5 Asset-Class Specific Performances
		4.5.6 Sensitivity to Trading Strategy
	4.6 Conclusions
	References
Chapter 5: Predictions of Industry Returns
	5.1 Introduction
	5.2 Data
		5.2.1 Summary Statistics
	5.3 Methodology
		5.3.1 Measuring Antifragility
		5.3.2 Predicting Industry Returns
	5.4 Results
		5.4.1 Long/Short Trading Strategy
		5.4.2 Regressions on Future Returns
		5.4.3 Comparisons to Classical Factors
		5.4.4 Separating Concavity and Convexity
		5.4.5 Separating Positive and Negative Market Returns
	5.5 Robustness Tests
		5.5.1 Stability of the Signal
		5.5.2 Sensitivity to Hyper-Parameters
	5.6 Conclusions
	References
Chapter 6: Predictions of Specific Returns
	6.1 Introduction
	6.2 Methodological Foundations
		6.2.1 Data
		6.2.2 Time-series Predictions, Cross-sectional Portfolio Construction
		6.2.3 Subtracting the Average Return
	6.3 Predictions Selection
		6.3.1 Root Mean Squared Error Filter Evaluation
		6.3.2 p-value Filter Evaluation
		6.3.3 Bayesian Information Criterion Filter Evaluation
		6.3.4 Selection Using Dynamic Optimal Filtering
	6.4 Aggregation of Predictions
		6.4.1 Prediction Aggregation Using Bayesian Model Averaging
		6.4.2 Prediction Aggregation Using Added Value Averaging
		6.4.3 Uncertainty of Aggregate Predictions
	6.5 Trading Strategy
		6.5.1 Methodology
		6.5.2 Performance
		6.5.3 Significance of the Methods
	6.6 Robustness Tests
		6.6.1 Correlations with Standard Factors
		6.6.2 Sensitivity to the Filter Regression Window
		6.6.3 Stability of the Performance
		6.6.4 Sensitivity to the Believability Measure Window
		6.6.5 Sensitivity to the Originality Measure Window
		6.6.6 Unaccounted Parameter Sensitivities
	6.7 Conclusions
	References
Chapter 7: Genetic Algorithm-Based Combination of Predictions
	7.1 Introduction
	7.2 Analysis of Strategies
		7.2.1 Predictions of Market Returns
		7.2.2 Predictions of Industry Returns
		7.2.3 Predictions of Specific Returns
		7.2.4 Correlation Analysis
	7.3 Risk Parity Combination
		7.3.1 Introducing Risk Parity
		7.3.2 Transaction Costs Matter
		7.3.3 Ex-ante Optimal Reduction of the Transaction Costs
	7.4 Genetic Algorithm-Based Combinations
		7.4.1 Methodology
		7.4.2 Results
	7.5 Robustness Tests
		7.5.1 Mutation Probability
		7.5.2 Number of Chromosomes
		7.5.3 Number of Epochs
		7.5.4 Seed
		7.5.5 Optimal Trading Rate Window
	7.6 Conclusions
	References
Chapter 8: Conclusions
Appendix
	Representative RMSE Distributions per Stock Index
	Market Timed Portfolio and Systemic Risk Indicator per Stock Index
	Industry Buckets Summary Statistics
	AFI Scores per Industry over Time