Financial Signal Processing and Machine Learning

1. Auflage 2016
ISBN: 978-1-118-74567-0
Verlag: Wiley

Buch, Englisch, 320 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 722 g

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches.
Key features:

- Highlights signal processing and machine learning as key approaches to quantitative finance.
- Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems.
- Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques.
- Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Autoren/Hrsg.

Akansu, Ali N

Kulkarni, Sanjeev R

Malioutov, Dmitry M

Inhaltsverzeichnis

List of Contributors xiii

Preface xv

1 Overview 1
Ali N. Akansu, Sanjeev R. Kulkarni, and Dmitry Malioutov

1.1 Introduction 1

1.2 A Bird’s-Eye View of Finance 2

1.2.1 Trading and Exchanges 4

1.2.2 Technical Themes in the Book 5

1.3 Overview of the Chapters 6

1.3.1 Chapter 2: “Sparse Markowitz Portfolios” by Christine De Mol 6

1.3.2 Chapter 3: “Mean-Reverting Portfolios: Tradeoffs between Sparsity and Volatility” by Marco Cuturi and Alexandre d’Aspremont 7

1.3.3 Chapter 4: “Temporal Causal Modeling” by Prabhanjan Kambadur, Aurélie C. Lozano, and Ronny Luss 7

1.3.4 Chapter 5: “Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process” by Mustafa U. Torun, Onur Yilmaz and Ali N. Akansu 7

1.3.5 Chapter 6: “Approaches to High-Dimensional Covariance and Precision Matrix Estimation” by Jianqing Fan, Yuan Liao, and Han Liu 7

1.3.6 Chapter 7: “Stochastic Volatility: Modeling and Asymptotic Approaches to Option Pricing and Portfolio Selection” by Matthew Lorig and Ronnie Sircar 7

1.3.7 Chapter 8: “Statistical Measures of Dependence for Financial Data” by David S. Matteson, Nicholas A. James, and William B. Nicholson 8

1.3.8 Chapter 9: “Correlated Poisson Processes and Their Applications in Financial Modeling” by Alexander Kreinin 8

1.3.9 Chapter 10: “CVaR Minimizations in Support Vector Machines” by Junya Gotoh and Akiko Takeda 8

1.3.10 Chapter 11: “Regression Models in Risk Management” by Stan Uryasev 8

1.4 Other Topics in Financial Signal Processing and Machine Learning 9

References 9

2 Sparse Markowitz Portfolios 11
ChristineDeMol

2.1 Markowitz Portfolios 11

2.2 Portfolio Optimization as an Inverse Problem: The Need for Regularization 13

2.3 Sparse Portfolios 15

2.4 Empirical Validation 17

2.5 Variations on the Theme 18

2.5.1 Portfolio Rebalancing 18

2.5.2 Portfolio Replication or Index Tracking 19

2.5.3 Other Penalties and Portfolio Norms 19

2.6 Optimal Forecast Combination 20

Acknowlegments 21

References 21

3 Mean-Reverting Portfolios 23
Marco Cuturi and Alexandre d’Aspremont

3.1 Introduction 23

3.1.1 Synthetic Mean-Reverting Baskets 24

3.1.2 Mean-Reverting Baskets with Sufficient Volatility and Sparsity 24

3.2 Proxies for Mean Reversion 25

3.2.1 Related Work and Problem Setting 25

3.2.2 Predictability 26

3.2.3 Portmanteau Criterion 27

3.2.4 Crossing Statistics 28

3.3 Optimal Baskets 28

3.3.1 Minimizing Predictability 29

3.3.2 Minimizing the Portmanteau Statistic 29

3.3.3 Minimizing the Crossing Statistic 29

3.4 Semidefinite Relaxations and Sparse Components 30

3.4.1 A Semidefinite Programming Approach to Basket Estimation 30

3.4.2 Predictability 30

3.4.3 Portmanteau 31

3.4.4 Crossing Stats 31

3.5 Numerical Experiments 32

3.5.1 Historical Data 32

3.5.2 Mean-reverting Basket Estimators 33

3.5.3 Jurek and Yang (2007) Trading Strategy 33

3.5.4 Transaction Costs 33

3.5.5 Experimental Setup 36

3.5.6 Results 36

3.6 Conclusion 39

References 39

4 Temporal Causal Modeling 41
Prabhanjan Kambadur, Aurélie C. Lozano, and Ronny Luss

4.1 Introduction 41

4.2 TCM 46

4.2.1 Granger Causality and Temporal Causal Modeling 46

4.2.2 Grouped Temporal Causal Modeling Method 47

4.2.3 Synthetic Experiments 49

4.3 Causal Strength Modeling 51

4.4 Quantile TCM (Q-TCM) 52

4.4.1 Modifying Group OMP for Quantile Loss 52

4.4.2 Experiments 53

4.5 TCM with Regime Change Identification 55

4.5.1 Model 56

4.5.2 Algorithm 58

4.5.3 Synthetic Experiments 60

4.5.4 Application: Analyzing Stock Returns 62

4.6 Conclusions 63

References 64

5 Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process 67
Mustafa U. Torun, Onur Yilmaz, and Ali N. Akansu

5.1 Introduction 67

5.2 Mathematical Definitions 68

5.2.1 Discrete AR(1) Stochastic Signal Model 68

5.2.2 Orthogonal Subspace 69

5.3 Derivation of Explicit KLT Kernel for a Discrete AR(1) Process 72

5.3.1 A Simple Method for Explicit Solution of a Transcendental Equation 73

5.3.2 Continuous Process with Exponential Autocorrelation 74

5.3.3 Eigenanalysis of a Discrete AR(1) Process 76

5.3.4 Fast Derivation of KLT Kernel for an AR(1) Process 79

5.4 Sparsity of Eigen Subspace 82

5.4.1 Overview of Sparsity Methods 83

5.4.2 pdf-Optimized Midtread Quantizer 84

5.4.3 Quantization of Eigen Subspace 86

5.4.4 pdf of Eigenvector 87

5.4.5 Sparse KLT Method 89

5.4.6 Sparsity Performance 91

5.5 Conclusions 97

References 97

6 Approaches to High-Dimensional Covariance and Precision Matrix Estimations 100
Jianqing Fan, Yuan Liao, and Han Liu

6.1 Introduction 100

6.2 Covariance Estimation via Factor Analysis 101

6.2.1 Known Factors 103

6.2.2 Unknown Factors 104

6.2.3 Choosing the Threshold 105

6.2.4 Asymptotic Results 105

6.2.5 A Numerical Illustration 107

6.3 Precision Matrix Estimation and Graphical Models 109

6.3.1 Column-wise Precision Matrix Estimation 110

6.3.2 The Need for Tuning-insensitive Procedures 111

6.3.3 TIGER: A Tuning-insensitive Approach for Optimal Precision Matrix Estimation 112

6.3.4 Computation 114

6.3.5 Theoretical Properties of TIGER 114

6.3.6 Applications to Modeling Stock Returns 115

6.3.7 Applications to Genomic Network 118

6.4 Financial Applications 119

6.4.1 Estimating Risks of Large Portfolios 119

6.4.2 Large Panel Test of Factor Pricing Models 121

6.5 Statistical Inference in Panel Data Models 126

6.5.1 Efficient Estimation in Pure Factor Models 126

6.5.2 Panel Data Model with Interactive Effects 127

6.5.3 Numerical Illustrations 130

6.6 Conclusions 131

References 131

7 Stochastic Volatility 135
Matthew Lorig and Ronnie Sircar

7.1 Introduction 135

7.1.1 Options and Implied Volatility 136

7.1.2 Volatility Modeling 137

7.2 Asymptotic Regimes and Approximations 141

7.2.1 Contract Asymptotics 142

7.2.2 Model Asymptotics 142

7.2.3 Implied Volatility Asymptotics 143

7.2.4 Tractable Models 145

7.2.5 Model Coefficient Polynomial Expansions 146

7.2.6 Small “Vol of Vol” Expansion 152

7.2.7 Separation of Timescales Approach 152

7.2.8 Comparison of the Expansion Schemes 154

7.3 Merton Problem with Stochastic Volatility: Model Coefficient Polynomial Expansions 155

7.3.1 Models and Dynamic Programming Equation 155

7.3.2 Asymptotic Approximation 157

7.3.3 Power Utility 159

7.4 Conclusions 160

Acknowledgements 160

References 160

8 Statistical Measures of Dependence for Financial Data 162
David S. Matteson, Nicholas A. James, and William B. Nicholson

8.1 Introduction 162

8.2 Robust Measures of Correlation and Autocorrelation 164

8.2.1 Transformations and Rank-Based Methods 166

8.2.2 Inference 169

8.2.3 Misspecification Testing 171

8.3 Multivariate Extensions 174

8.3.1 Multivariate Volatility 175

8.3.2 Multivariate Misspecification Testing 176

8.3.3 Granger Causality 176

8.3.4 Nonlinear Granger Causality 177

8.4 Copulas 179

8.4.1 Fitting Copula Models 180

8.4.2 Parametric Copulas 181

8.4.3 Extending beyond Two Random Variables 183

8.4.4 Software 185

8.5 Types of Dependence 185

8.5.1 Positive and Negative Dependence 185

Über Autor(innen)

Ali N. Akansu, Electrical and Computer Engineering Department, New Jersey Institute of Technology (NJIT), USA
Dr. Akansu is a Professor of Electrical and Computer Engineering at NJIT, USA. Prof. Akansu was VP R&D at IDT Corporation and the founding President and CEO of PixWave, Inc. He has sat on the board of an investment fund and has been an academic visitor at David Sarnoff Research Center, IBM T.J. Watson Research Center, and GEC-Marconi Electronic Systems.Prof. Akansu was a Visiting Professor at Courant Institute of Mathematical Sciences of New York University performing research on Quantitative Finance. He is a Fellow of the IEEE and was the Lead Guest Editor of the recent special issue of IEEE Journal of Selected Topics in Signal Processing on Signal Processing Methods in Finance and Electronic Trading.

Sanjeev R. Kulkarni, Department of Electrical Engineering, Princeton University, USA
Dr. Kulkarni is currently Professor of Electrical Engineering at Princeton University, and Director of Princeton’s Keller Center. He is an affiliated faculty member of the Department of Operations Research and Financial Engineering and the Department of Philosophy, and has taught a broad range of courses across a number of departments (Electrical Engineering, Computer Science, Philosophy, and Operations Research & Financial Engineering). He has received 7 E-Council Excellence in Teaching Awards. He spent 1998 with Susquehanna International Group and was a regular consultant there from 1997 to 2001, working on statistical arbitrage and analysis of firm-wide stock trading. Prof. Kulkarni is a Fellow of the IEEE.

Dmitry Malioutev, IBM Research, USA
Dr. Dmitry Malioutov is a research staff member in the machine learning group of the Cognitive Algorithms department at IBM Research. Dmitry received the Ph.D. and the S.M. degrees in Electrical Engineering and Computer Science from MIT where he was part of the Laboratory for Information and Decision Systems. Prior to joining IBM, Dmitry had spent several years as an applied researcher in high-frequency trading in DRW Trading, Chicago, and as a postdoctoral researcher in Microsoft Research, Cambridge, UK. His research interests include interpretable machine learning; sparse signal representation; inference and learning in graphical models, message passing algorithms; Statistical risk modeling, robust covariance estimation; portfolio optimization. Dr. Malioutov received the 2010 IEEE Signal Processing Society best 5-year paper award, and a 2006 IEEE ICASSP student paper award, and the MIT Presidential fellowship. Dr. Malioutov serves on the IEEE-SPS machine learning for signal processing technical committee, and is an associate editor of the IEEE Transactions on Signal Processing, and a guest editor of the IEEE Journal on Selected Topics in Signal Processing.