Financial Signal Processing and Machine Learning

Ali N. Akansu (Editor), Sanjeev R. Kulkarni (Editor), Dmitry M. Malioutov (Editor)

  • 出版商: IEEE
  • 出版日期: 2016-05-31
  • 定價: $3,700
  • 售價: 9.0$3,330
  • 語言: 英文
  • 頁數: 312
  • 裝訂: Hardcover
  • ISBN: 1118745671
  • ISBN-13: 9781118745670
  • 相關分類: Machine Learning
  • 立即出貨 (庫存 < 3)


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.



- 強調信號處理和機器學習作為量化金融的關鍵方法。
- 提供高維度投資組合構建、監控和交易後分析問題的高級數學工具。
- 提出投資組合理論、稀疏學習和壓縮感知、稀疏方法用於投資組合,包括特徵組合、回報模型、動能、均值回歸和非高斯數據驅動的風險度量,並應用於實際場景。
- 包括來自信號和信息處理社區以及量化金融社區的領先研究人員和從業者的貢獻。