Sprache: Englisch
Verlag: Wiley & Sons, Incorporated, John, 2016
ISBN 10: 1118745671 ISBN 13: 9781118745670
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Zustand: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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In den WarenkorbZustand: New. pp. 448.
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 320 pages. 9.75x7.00x1.00 inches. In Stock.
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In den WarenkorbZustand: New. 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.KlappentextThe modern financial industry has been required to deal with .
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In den WarenkorbZustand: New. 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. Editor(s): Akansu, Ali N.; Kulkarni, Sanjeev R.; Malioutov, Dmitry M.; Pollak, Ilya. Series: Wiley - IEEE. Num Pages: 320 pages, illustrations. BIC Classification: TJK; UYQM; UYS. Category: (P) Professional & Vocational. Dimension: 178 x 251 x 19. Weight in Grams: 626. . 2016. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Buch. Zustand: Neu. Neuware - 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.