A Signal Processing Perspective of Financial Engineering PDF Download

Author: Yiyong Feng
Publisher: Now Publishers
ISBN: 9781680831184
Category : Technology & Engineering
Languages : en
Pages : 256

View

Book Description
A Signal Processing Perspective of Financial Engineering provides straightforward and systematic access to financial engineering for researchers in signal processing and communications

Author: Yiyong Feng
Publisher:
ISBN: 9781680831191
Category : Adaptive signal processing
Languages : en
Pages : 231

View

Book Description
Financial engineering and electrical engineering are seemingly different areas that share strong underlying connections. Both areas rely on statistical analysis and modeling of systems; either modeling the financial markets or modeling wireless communication channels. Having a model of reality allows us to make predictions and to optimize the strategies. It is as important to optimize our investment strategies in a financial market as it is to optimize the signal transmitted by an antenna in a wireless link. This monograph provides a survey of financial engineering from a signal processing perspective, that is, it reviews financial modeling, the design of quantitative investment strategies, and order execution with comparison to seemingly different problems in signal processing and communication systems, such as signal modeling, filter/beamforming design, network scheduling, and power allocation.

Author: Ali N. Akansu
Publisher: Academic Press
ISBN: 0128017503
Category : Technology & Engineering
Languages : en
Pages : 156

View

Book Description
This book bridges the fields of finance, mathematical finance and engineering, and is suitable for engineers and computer scientists who are looking to apply engineering principles to financial markets. The book builds from the fundamentals, with the help of simple examples, clearly explaining the concepts to the level needed by an engineer, while showing their practical significance. Topics covered include an in depth examination of market microstructure and trading, a detailed explanation of High Frequency Trading and the 2010 Flash Crash, risk analysis and management, popular trading strategies and their characteristics, and High Performance DSP and Financial Computing. The book has many examples to explain financial concepts, and the presentation is enhanced with the visual representation of relevant market data. It provides relevant MATLAB codes for readers to further their study. Please visit the companion website on http://booksite.elsevier.com/9780128015612/ Provides engineering perspective to financial problems In depth coverage of market microstructure Detailed explanation of High Frequency Trading and 2010 Flash Crash Explores risk analysis and management Covers high performance DSP & financial computing

Author: Ali N. Akansu
Publisher: John Wiley & Sons
ISBN: 1118745639
Category : Technology & Engineering
Languages : en
Pages : 312

View

Book Description
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.

Author: Ali N. Akansu
Publisher: Wiley-IEEE Press
ISBN: 9781118745618
Category : Technology & Engineering
Languages : en
Pages : 312

View

Book Description
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.

Author: Serges Darolles
Publisher: John Wiley & Sons
ISBN: 1118577493
Category : Technology & Engineering
Languages : en
Pages : 113

View

Book Description
With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages “embedded” quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented “risk assessment-based” practices. This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an interesting alternative to the selection of factors (both fundamentals and statistical factors) and can provide more efficient estimation procedures, based on lq regularized Kalman filtering for instance. With numerous illustrative examples from stock markets, this book meets the needs of both finance practitioners and graduate students in science, econometrics and finance. Contents Foreword, Rama Cont. 1. Factor Models and General Definition. 2. Factor Selection. 3. Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective. 4. A Regularized Kalman Filter (rgKF) for Spiky Data. Appendix: Some Probability Densities. About the Authors Serge Darolles is Professor of Finance at Paris-Dauphine University, Vice-President of QuantValley, co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His research interests include financial econometrics, liquidity and hedge fund analysis. He has written numerous articles, which have been published in academic journals. Patrick Duvaut is currently the Research Director of Telecom ParisTech, France. He is co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His fields of expertise encompass statistical signal processing, digital communications, embedded systems and QUANT finance. Emmanuelle Jay is co-founder and President of QAMLab SAS. She has worked at Aequam Capital as co-head of R&D since April 2011 and is member of the Quantitative Management Initiative (QMI) scientific committee. Her research interests include SP for finance, quantitative and statistical finance, and hedge fund analysis.

Author: Larry D. Paarmann
Publisher: Springer Science & Business Media
ISBN: 0306480123
Category : Technology & Engineering
Languages : en
Pages : 440

View

Book Description
Design and Analysis of Analog Filters: A Signal Processing Perspective includes signal processing/systems concepts as well as implementation. While most books on analog filter design briefly present the signal processing/systems concepts, and then concentrate on a variety of filter implementation methods, the present book reverses the emphasis, stressing signal processing concepts. Filter implementation topics are presented in Part II: passive filters, and operational amplifier active filters. However, greater emphasis on signal processing/systems concepts is included in Part I of the book than is typical. This emphasis makes the book very appropriate as part of a signal processing curriculum. Useful Aspects of Design and Analysis of Analog Filters: A Signal Processing Perspective extensive use of MATLAB® throughout, with many homework problems involving the use of MATLAB. over 200 figures; over 100 examples; a total of 345 homework problems, appearing at the ends of the chapters; complete and thorough presentation of design characteristics; complete catalog of design approaches. Audience: Design and Analysis of Analog Filters: A Signal Processing Perspective will interest anyone with a standard electrical engineering background, with a B.S. degree or beyond, or at the senior level. While designed as a textbook, its numerous practical examples make it useful as a reference for practicing engineers and scientists, particularly those working in systems design or communications. MATLAB® Examples: A valuable relationship between analog filter theory and analysis and modern digital signal processing is made by the application of MATLAB to both the design and analysis of analog filters. Throughout the book, computer-oriented problems are assigned. The disk that accompanies this book contains MATLAB functions and m-files written specifically for this book. The MATLAB functions on the disk extend basic MATLAB capabilities in terms of the design and analysis of analog filters. The m-files are used in a number of examples in the book. They are included on the disk as an instructional aid.

Author: Edward R. Dougherty
Publisher: Hindawi Publishing Corporation
ISBN: 9775945070
Category : DNA microarrays
Languages : en
Pages : 456

View

Book Description
Recent advances in genomic studies have stimulated synergetic research and development in many cross-disciplinary areas. Processing the vast genomic data, especially the recent large-scale microarray gene expression data, to reveal the complex biological functionality, represents enormous challenges to signal processing and statistics. This perspective naturally leads to a new field, genomic signal processing (GSP), which studies the processing of genomic signals by integrating the theory of signal processing and statistics. Written by an international, interdisciplinary team of authors, this invaluable edited volume is accessible to students just entering this emergent field, and to researchers, both in academia and in industry, in the fields of molecular biology, engineering, statistics, and signal processing. The book provides tutorial-level overviews and addresses the specific needs of genomic signal processing students and researchers as a reference book. The book aims to address current genomic challenges by exploiting potential synergies between genomics, signal processing, and statistics, with special emphasis on signal processing and statistical tools for structural and functional understanding of genomic data. The first part of this book provides a brief history of genomic research and a background introduction from both biological and signal-processing/statistical perspectives, so that readers can easily follow the material presented in the rest of the book. In what follows, overviews of state-of-the-art techniques are provided. We start with a chapter on sequence analysis, and follow with chapters on feature selection, classification, and clustering of microarray data. We then discuss the modeling, analysis, and simulation of biological regulatory networks, especially gene regulatory networks based on Boolean and Bayesian approaches. Visualization and compression of gene data, and supercomputer implementation of genomic signal processing systems are also treated. Finally, we discuss systems biology and medical applications of genomic research as well as the future trends in genomic signal processing and statistics research.

Author: Greg Edlund
Publisher: Pearson Education
ISBN: 0132797186
Category : Technology & Engineering
Languages : en
Pages : 271

View

Book Description
Every day, companies call upon their signal integrity engineers to make difficult decisions about design constraints and timing margins. Can I move these wires closer together? How many holes can I drill in this net? How far apart can I place these chips? Each design is unique: there’s no single recipe that answers all the questions. Today’s designs require ever greater precision, but design guides for specific digital interfaces are by nature conservative. Now, for the first time, there’s a complete guide to timing analysis and simulation that will help you manage the tradeoffs between signal integrity, performance, and cost. Writing from the perspective of a practicing SI engineer and team lead, Greg Edlund of IBM presents deep knowledge and quantitative techniques for making better decisions about digital interface design. Edlund shares his insights into how and why digital interfaces fail, revealing how fundamental sources of pathological effects can combine to create fault conditions. You won’t just learn Edlund’s expert techniques for avoiding failures: you’ll learn how to develop the right approach for your own projects and environment. Coverage includes • Systematically ensure that interfaces will operate with positive timing margin over the product’s lifetime–without incurring excess cost • Understand essential chip-to-chip timing concepts in the context of signal integrity • Collect the right information upfront, so you can analyze new designs more effectively • Review the circuits that store information in CMOS state machines–and how they fail • Learn how to time common-clock, source synchronous, and high-speed serial transfers • Thoroughly understand how interconnect electrical characteristics affect timing: propagation delay, impedance profile, crosstalk, resonances, and frequency-dependent loss • Model 3D discontinuities using electromagnetic field solvers • Walk through four case studies: coupled differential vias, land grid array connector, DDR2 memory data transfer, and PCI Express channel • Appendices present a refresher on SPICE modeling and a high-level conceptual framework for electromagnetic field behavior Objective, realistic, and practical, this is the signal integrity resource engineers have been searching for. Preface xiii Acknowledgments xvi About the Author xix About the Cover xx Chapter 1: Engineering Reliable Digital Interfaces 1 Chapter 2: Chip-to-Chip Timing 13 Chapter 3: Inside IO Circuits 39 Chapter 4: Modeling 3D Discontinuities 73 Chapter 5: Practical 3D Examples 101 Chapter 6: DDR2 Case Study 133 Chapter 7: PCI Express Case Study 175 Appendix A: A Short CMOS and SPICE Primer 209 Appendix B: A Stroll Through 3D Fields 219 Endnotes 233 Index 235

Author: Michael Robinson
Publisher: Springer Science & Business Media
ISBN: 3642361048
Category : Technology & Engineering
Languages : en
Pages : 245

View

Book Description
Signal processing is the discipline of extracting information from collections of measurements. To be effective, the measurements must be organized and then filtered, detected, or transformed to expose the desired information. Distortions caused by uncertainty, noise, and clutter degrade the performance of practical signal processing systems. In aggressively uncertain situations, the full truth about an underlying signal cannot be known. This book develops the theory and practice of signal processing systems for these situations that extract useful, qualitative information using the mathematics of topology -- the study of spaces under continuous transformations. Since the collection of continuous transformations is large and varied, tools which are topologically-motivated are automatically insensitive to substantial distortion. The target audience comprises practitioners as well as researchers, but the book may also be beneficial for graduate students.