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Financial Signal Processing and Machine Learning, A.N. Akansu, S.R. Kulkarni and D.M. Malioutov, Eds. Wiley-IEEE Press, 2016.
Description: 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.
|Nominations Open for 2020 SPS Awards||1 September 2020|
|Call for Nominations: Awards Board and Nominations and Appointments Committee||25 September 2020|
|Call for Nominations: Fellow Evaluation Committee||30 September 2020|
|Election of Regional Directors-at-Large and Members-at-Large||1 October 2020|
|Meet the 2020 Candidates: IEEE President-Elect and Division IX Director-Elect||1 October 2020|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2020|
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