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. <i>Financial Signal Processing and Machine Learning</i> 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,
Pris: kr 1048.00 fra Norli
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kr 1048.00 | Besøk butikk |
<p><b>Machine Learning: A Bayesian and Optimization Perspective, 2<SUP>nd</SUP> edition</b>, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification....
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<p>The authors offer a comprehensive guide to machine learning applied to signal processing and recognition problems, and then discuss real applications in domains such as speech processing and biomedical signal processing, with a focus on handling...
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Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts...
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