Ziping Zhao
Signal processing for finance
The application of research ideas from theoretical physics, mathematics, and control theory to the financial markets has been a common industrial practice for now almost three decades. Signal processing, without exception, has benefited financial engineering substantially through well-known and widely applied techniques as well, to name a few, the Fourier transform, the Kalman filter, and shrinkage methods. The connection between signal processing and finance is becoming more and more evident. Below you can find a (non-exhaustive) list of useful resources in the field of signal processing for finance.
Conferences/Workshops/Special Issues
IEEE ICASSP 2020 Special Session on Signal Processing Methods for Finance Applications
IEEE GlobalSIP 2019 Symposium on Signal, Information Processing, and AI for Finance and Business
IEEE ICASSP 2019 Special Session on Signal Processing for Emerging Applications: FinTech
IEEE GlobalSIP 2017 Symposium on Signal and Information Processing for Finance and Business
IEEE GlobalSIP 2013 Symposium on Signal and Information Processing in Finance and Economics
Asilomar 2013 Special Session on Financial Engineering and Signal Processing
IEEE SPM Special Issue on Signal Processing for Financial Applications (September 2011)
IEEE ICASSP 2011 Special Session on Signal Processing Methods for Finance Applications
IEEE CAMSAP 2011 Special Session on Signal Processing Applications to Financial Engineering
IEEE ICASSP 2010 Special Session on Signal Processing Methods for Finance Applications
Books/Book chapters/Monographs
K. Benidis, Y. Feng, and D. P. Palomar, “Optimization methods for financial index tracking: From theory to practice,” Foundations and Trends in Optim., vol. 3, no. 3, pp. 171–279, 2018.
A. N. Akansu, S. R. Kulkarni, and D. Malioutov, “Financial Signal Processing and Machine Learning,” John Wiley & Sons, Ltd, 2016.
Y. Feng and D. P. Palomar, “A signal processing perspective on financial engineering,” Foundations and Trends in Signal Process., vol. 9, no. 1–2, pp. 1–231, 2016.
A. N. Akansu and M. U. Torun, “A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading,” Elsevier, 2015.
F. Benedetto, G. Giunta, and L. Mastroeni, “Signal processing for financial markets,” In Encyclopedia of Information Science and Technology, Third Edition, 7339–7346, IGI Global, 2015.
Reviews/Tutorials
Introductory article
X.-P. S. Zhang and F. Wang, “Signal processing for finance, economics, and marketing: Concepts, framework, and big data applications,” IEEE Signal Process. Mag., vol. 34, no. 3, pp. 14–35, May 2017.
I. Pollak, “Incorporating financial applications in signal processing curricula,” IEEE Signal Process. Mag., vol. 28, pp. 122–125, Sept. 2011.
D. E. Johnston and P. M. Djuric, “The science behind risk management,” IEEE Signal Process. Mag., vol. 28, pp. 26–36, Sept. 2011.
N. Gradojevic and R. Gencay, “Financial applications of nonextensive entropy,” IEEE Signal Process. Mag., vol. 28, no. 5, pp. 116–141, May 2011.
K. Drakakis, “Application of signal processing to the analysis of financial data,” IEEE Signal Process. Mag., vol. 26, pp. 160–158, Sept. 2009.
Overview/Keynote talks
D. P. Palomar, “High-Order Portfolios: The Role of Heavy Tails and Skewness”, SLSIP Workshop 2022 Plenary Talk.
M. M. Veloso, “AI in Finance: Data, Reasoning, and Values”, IEEE ICASSP 2021 Plenary Talk.
D. P. Palomar, “Learning graphs of stocks: From iid to time-varying models”, IEEE GSP 2020 Plenary Talk.
D. Li, “From Speech AI to Finance AI and Back”, IEEE ICASSP 2020 Keynote Talk.
D. P. Palomar, “Portfolio Optimization in Financial Markets”, IEEE EUSIPCO 2019 Tutorial.
X.-P. S. Zhang, “Applying Signal Processing and Machine Learning to Finance, Economics, and Marketing”, IEEE SPS Webinar, August, 2018.
D. P. Palomar, “Financial Engineering Playground: Signal Processing, Robust Estimation, Kalman, Optimization”, IEEE SSP 2018 Plenary Talk.
X.-P. S. Zhang, “Fundamentals on Signal Processing for Finance and Economics”, IEEE SPS Webinar, March, 2018.
D. P. Palomar, “A Signal Processing and Optimization Perspective on Financial Engineering”, IEEE CAMSAP 2017 Plenary Talk.
D. P. Palomar and Y. Feng, “A Signal Processing Perspective on Financial Engineering”, IEEE ICASSP 2017 Tutorial.
D. P. Palomar and Y. Feng, “A Signal Processing Perspective on Financial Engineering”, IEEE ICASSP 2016 Tutorial.
A. N. Akansu and D. Malioutov, “Covariance Analysis and Machine Learning Methods for Electronic Trading”, IEEE ICASSP 2015 Tutorial.
X.-P. S. Zhang and F. Wang, “Signal Processing for Finance, Economics and Marketing Modeling and Information Processing”, IEEE ICASSP 2014 Tutorial.
A. N. Akansu and I. Pollak, “High Frequency Trading and Signal Processing Models for the Microstructure of Financial Markets”, IEEE ICASSP 2013 Tutorial.
E. Fishler, “Electrical Engineering and Quantitative Finance: A Tale of Two Seemingly Unrelated Disciplines”, IEEE GlobalSIP 2013 Keynote.
K. R. Varshney, “Introduction to Business Analytics", IEEE ICASSP 2012 Tutorial.
Machine learning for finance
Similar to signal processing, machine learning, commonly regarded as a discipline with data-driven methods, has a huge number of applications in finance. Below you can find a (non-exhaustive) list of useful resources in the field of machine learning for finance.
Conferences/Workshops/Special Issues
ICML 2021 Workshop on Representation Learning for Finance and E-Commerce Applications
NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services
IEEE TNN Special Issue on Neural Networks in Financial Engineering (July 2001)
Books/Book chapters/Monographs
M. F. Dixon, I. Halperin, and P. Bilokon, “Machine learning in finance,” Springer, 2020.
M. L. de Prado, “Advances in Financial Machine Learning,” Wiley, 2018.
Reviews/Tutorials
D. P. Palomar, “Learning Financial Graphs”, ACM ICAIF 2022 Workshop on NLP and Network Analysis in Financial Applications Keynote.
D. P. Palomar, “Signal Processing and Optimization in Financial Engineering”, EUROCAST 2019 Tutorial.