Ali N. Akansu

Sanjeev R. Kulkarni

Dmitry M. Malioutov

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.

Financial Signal Processing and Machine Learning

Michael J. Medley

Wavelet and subband transforms have been of great interest in the fields of - gineering and applied mathematics. The theories of these powerful signal p- cessing tools have matured and many applications utilizing them are emerging in different disciplines. This book, comprised of eleven chapter contributions from prominent researchers in the field, focuses on communications and mul- media applications of wavelet and subband transforms. The first six chapters of this book deal with a variety of communications applications that significantly benefit from wavelet and subband theories. S- ilarly, the remaining five chapters present recent advances in multimedia - plications of wavelet and subband transforms. These chapters interconnect the requirements of applications with the underlying theory and their engineering solutions. Hence, the reader can easily trace the entire path from fundamentals to the purpose and merit of application in hand. A combined list of references for the entire volume is given at the end of the text that should be helpful to the interested reader for a further study. This book is anticipated to be of particular interest to engineers and sci- tists who want to learn about state-of-the-art subband and wavelet transform applications as well as their theoretical underpinnings. It can also serve as a supplementary book for graduate level engineering and applied mathematics courses on wavelet and subband transforms.

Wavelet, Subband and Block Transforms in Communications and Multimedia

Husrev T. Sencar

Ramkumar Mahalingam

Multimedia technologies are becoming more sophisticated, enabling the Internet to accommodate a rapidly growing audience with a full range of services and efficient delivery methods. Although the Internet now puts communication, education, commerce and socialization at our finger tips, its rapid growth has raised some weighty security concerns with respect to multimedia content. The owners of this content face enormous challenges in safeguarding their intellectual property, while still exploiting the Internet as an important resource for commerce. Data Hiding Fundamentals and Applications focuses on the theory and state-of-the-art applications of content security and data hiding in digital multimedia. One of the pillars of content security solutions is the imperceptible insertion of information into multimedia data for security purposes; the idea is that this inserted information will allow detection of unauthorized usage. * Provides a theoretical framework for data hiding, in a signal processing context; * Realistic applications in secure, multimedia delivery; * Compression robust data hiding; * Data hiding for proof of ownership--WATERMARKING; * Data hiding algorithms for image and video watermarking.

Ali N. Akansu

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