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Financial signal processing and machine learning /
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 th...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
West Sussex, United Kingdom :
Wiley : IEEE Press,
2016.
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| Colección: | Wiley - IEEE
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Financial signal processing and machine learning / |c edited by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov. |
| 264 | 1 | |a West Sussex, United Kingdom : |b Wiley : |b IEEE Press, |c 2016. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Wiley - IEEE | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (Wiley, viewed May 11, 2016). | |
| 520 | |a 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. | ||
| 505 | 0 | |a Title Page; Copyright; Table of Contents; List of Contributors; Preface; Chapter 1: Overview; 1.1 Introduction; 1.2 A Bird's-Eye View of Finance; 1.3 Overview of the Chapters; 1.4 Other Topics in Financial Signal Processing and Machine Learning; References; Chapter 2: Sparse Markowitz Portfolios; 2.1 Markowitz Portfolios; 2.2 Portfolio Optimization as an Inverse Problem: The Need for Regularization; 2.3 Sparse Portfolios; 2.4 Empirical Validation; 2.5 Variations on the Theme; 2.6 Optimal Forecast Combination; Acknowlegments; References; Chapter 3: Mean-Reverting Portfolios; 3.1 Introduction. | |
| 505 | 8 | |a 3.2 Proxies for Mean Reversion3.3 Optimal Baskets; 3.4 Semidefinite Relaxations and Sparse Components; 3.5 Numerical Experiments; 3.6 Conclusion; References; Chapter 4: Temporal Causal Modeling; 4.1 Introduction; 4.2 TCM; 4.3 Causal Strength Modeling; 4.4 Quantile TCM (Q-TCM); 4.5 TCM with Regime Change Identification; 4.6 Conclusions; References; Chapter 5: Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process; 5.1 Introduction; 5.2 Mathematical Definitions; 5.3 Derivation of Explicit KLT Kernel for a Discrete AR(1) Process; 5.4 Sparsity of Eigen Subspace; 5.5 Conclusions. | |
| 505 | 8 | |a Chapter 8: Statistical Measures of Dependence for Financial Data8.1 Introduction; 8.2 Robust Measures of Correlation and Autocorrelation; 8.3 Multivariate Extensions; 8.4 Copulas; 8.5 Types of Dependence; References; Chapter 9: Correlated Poisson Processes and Their Applications in Financial Modeling; 9.1 Introduction; 9.2 Poisson Processes and Financial Scenarios; 9.3 Common Shock Model and Randomization of Intensities; 9.4 Simulation of Poisson Processes; 9.5 Extreme Joint Distribution; 9.6 Numerical Results; 9.7 Backward Simulation of the Poisson-Wiener Process; 9.8 Concluding Remarks. | |
| 505 | 8 | |a AcknowledgmentsAppendix A; References; Chapter 10: CVaR Minimizations in Support Vector Machines; 10.1 What Is CVaR?; 10.2 Support Vector Machines; 10.3 v-SVMs as CVaR Minimizations; 10.4 Duality; 10.5 Extensions to Robust Optimization Modelings; 10.6 Literature Review; References; Chapter 11: Regression Models in Risk Management; 11.1 Introduction; 11.2 Error and Deviation Measures; 11.3 Risk Envelopes and Risk Identifiers; 11.4 Error Decomposition in Regression; 11.5 Least-Squares Linear Regression; 11.6 Median Regression; 11.7 Quantile Regression and Mixed Quantile Regression. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Signal processing |x Digital techniques. | |
| 650 | 4 | |a Finance. | |
| 650 | 4 | |a Business & Economics |x Finance |x General. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Traitement du signal |x Techniques numériques. | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Signal processing |x Digital techniques |2 fast | |
| 700 | 1 | |a Akansu, Ali N., |d 1958- |e editor. |1 https://id.oclc.org/worldcat/entity/E39PBJxhjWG8cqJWjTdpQ38jYP | |
| 700 | 1 | |a Kulkarni, Sanjeev, |e editor. | |
| 700 | 1 | |a Malioutov, Dmitry, |e editor. | |
| 758 | |i has work: |a Financial signal processing and machine learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGfwxGWhWbYdcG4YDYtmbb |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Financial signal processing and machine learning. |d Chichester, England : IEEE Press : Wiley, ©2016 |h approximately 384 pages |z 9781118745540 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4513064 |z Texto completo |
| 880 | 8 | |6 505-00/(3/r |a 9.5.2 Monotone Distributions 208 -- 9.5.3 Computation of the Joint Distribution 214 -- 9.5.4 On the Frechet-Hoeffding Theorem 215 -- 9.5.5 Approximation of the Extreme Distributions 217 -- 9.6 Numerical Results 219 -- 9.6.1 Examples of the Support 219 -- 9.6.2 Correlation Boundaries 221 -- 9.7 Backward Simulation of the Poisson-Wiener Process 222 -- 9.8 Concluding Remarks 227 -- Acknowledgments 228 -- Appendix A 229 -- A.1 Proof of Lemmas 9.2 and 9.3 229 -- A.1.1 Proof of Lemma 9.2 229 -- A.1.2 Proof of Lemma 9.3 230 -- References 231 -- 10 CVaR Minimizations in Support Vector Machines 233 /Jun-ya Gotoh and Akiko Takeda -- 10.1 What Is CVaR234 -- 10.1.1 Definition and Interpretations 234 -- 10.1.2 Basic Properties of CVaR 238 -- 10.1.3 Minimization of CVaR 240 -- 10.2 Support Vector Machines 242 -- 10.2.1 Classification 242 -- 10.2.2 Regression 246 -- 10.3 ̧حج-SVMs as CVaR Minimizations 247 -- 10.3.1 ̧حج-SVMs as CVaR Minimizations with Homogeneous Loss 247 -- 10.3.2 ̧حج-SVMs as CVaR Minimizations with Nonhomogeneous Loss 251 -- 10.3.3 Refining the ̧حج-Property 253 -- 10.4 Duality 256 -- 10.4.1 Binary Classification 256 -- 10.4.2 Geometric Interpretation of ̧حج-SVM 257 -- 10.4.3 Geometric Interpretation of the Range of ̧حج for ̧حج-SVC 258 -- 10.4.4 Regression 259 -- 10.4.5 One-class Classification and SVDD 259 -- 10.5 Extensions to Robust Optimization Modelings 259 -- 10.5.1 Distributionally Robust Formulation 259 -- 10.5.2 Measurement-wise Robust Formulation 261 -- 10.6 Literature Review 262 -- 10.6.1 CVaR as a Risk Measure 263 -- 10.6.2 From CVaR Minimization to SVM 263 -- 10.6.3 From SVM to CVaR Minimization 263 -- 10.6.4 Beyond CVaR 263 -- References 264 -- 11 Regression Models in Risk Management 266 /Stan Uryasev -- 11.1 Introduction 267 -- 11.2 Error and Deviation Measures 268 -- 11.3 Risk Envelopes and Risk Identifiers 271 -- 11.3.1 Examples of Deviation Measures D, Corresponding Risk Envelopes Q, and Sets of Risk Identifiers QD(X) 272 -- 11.4 Error Decomposition in Regression 273. | |
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