Overview: Financial Signal Processing and Machine Learning

Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov

Research output: Chapter in Book/Report/Conference proceedingForeword/postscript

Abstract

This introductory chapter presents a brief summary of basic concepts in finance and risk management, and provides overview of the concepts discussed in the chapters of this book. It provides the underlying technical themes, including sparse learning, convex optimization, and non-Gaussian modeling. Finance broadly deals with all aspects of money management, including borrowing and lending, transfer of money across continents, investment and price discovery, and asset and liability management by governments, corporations, and individuals. A unifying challenge for many applications of signal processing and machine learning is the high-dimensional nature of the data, and the need to exploit the inherent structure in those data. The book focuses on a set of topics revolving around the concepts of high-dimensional covariance estimation, applications of sparse learning in risk management and statistical arbitrage, and non-Gaussian and heavy-tailed measures of dependence.

Original languageEnglish (US)
Title of host publicationFinancial Signal Processing and Machine Learning
PublisherWiley-IEEE Press
Pages1-10
Number of pages10
ISBN (Electronic)9781118745540
ISBN (Print)9781118745670
DOIs
StatePublished - Apr 29 2016

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science(all)

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    Akansu, A. N., Kulkarni, S. R., & Malioutov, D. (2016). Overview: Financial Signal Processing and Machine Learning. In Financial Signal Processing and Machine Learning (pp. 1-10). Wiley-IEEE Press. https://doi.org/10.1002/9781118745540.ch1