Financial signal processing machine (22 résultats)
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EUR 82,69
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EUR 83,59
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Financial Signal Processing and Machine Learning
Akansu, Ali N. (EDT); Kulkarni, Sanjeev R. (EDT); Malioutov, Dmitry (EDT)
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EUR 104,64
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EUR 101,27
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EUR 97,46
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Financial Signal Processing and Machine Learning
Akansu, Ali N. (EDT); Kulkarni, Sanjeev R. (EDT); Malioutov, Dmitry (EDT)
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EUR 100,66
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Etat : New. In.
Financial Signal Processing and Machine Learning
Akansu, Ali N. (EDT); Kulkarni, Sanjeev R. (EDT); Malioutov, Dmitry (EDT)
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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EUR 100,65
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EUR 118,42
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Etat : New.
Financial Signal Processing and Machine Learning
Akansu, Ali N. (EDT); Kulkarni, Sanjeev R. (EDT); Malioutov, Dmitry (EDT)
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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EUR 108,49
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EUR 126,63
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Etat : New. pp. 448.
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- Édition originale
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandeKennys Bookshop and Art Galleries Ltd.
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EUR 124,54
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Etat : 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 Classifi…cation: TJK; UYQM; UYS. Category: (P) Professional & Vocational. Dimension: 178 x 251 x 19. Weight in Grams: 626. . 2016. 1st Edition. Hardcover. . . . .
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Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
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EUR 140,67
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Etat : New. pp. 448.
Financial Signal Processing and Machine Learning
Akansu, Ali N. (Editor)/ Kulkarni, Sanjeev R. (Editor)/ Malioutov, Dmitry (Editor)
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Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
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EUR 147,36
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Hardcover. Etat : Brand New. 1st edition. 320 pages. 9.75x7.00x1.00 inches. In Stock.
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Vendeur : moluna, Greven, Allemagnemoluna
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EUR 111,01
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Etat : 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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Vendeur : Kennys Bookstore, Olney, MD, Etats-UnisKennys Bookstore
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EUR 156,32
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Etat : 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 Classifi…cation: 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.
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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EUR 137,13
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Buch. Etat : 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. 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.
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- impression à la demande
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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EUR 107,00
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Hardcover. Etat : new. Hardcover. 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 fo…r 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller
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EUR 118,23
EUR 31,99 expéditionExpédition depuis Australie vers Etats-UnisQuantité disponible : 1 disponible(s)
Hardcover. Etat : new. Hardcover. 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 fo…r 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Financial Signal Processing and Machine Learning
Akansu, Ali N. (Editor)/ Kulkarni, Sanjeev R. (Editor)/ Malioutov, Dmitry (Editor)
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- impression à la demande
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 137,64
EUR 14,62 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 2 disponible(s)
Hardcover. Etat : Brand New. 1st edition. 320 pages. 9.75x7.00x1.00 inches. In Stock. This item is printed on demand.
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- Édition originale
- impression à la demande
Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
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EUR 108,43
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Hardcover. Etat : new. Hardcover. 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 fo…r 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
