Advanced Bio-Inspired Mathematical Modeling and Machine Learning Algorithms for Quantitative Finance Applications

Dear Colleagues,

This Special Issue welcomes paper submissions from all areas of quantitative finance, with a special focus on the research articles showing the development of advanced bio-inspired mathematical models and algorithms for stocks trading as well as for financial time-series analysis.

There is growing interest in applying bio-inspired mathematical models and recent machine learning algorithms to address different financial problems, especially having regard to the large amount of data that these algorithms will have to analyze in real time.

The advantages of use the recent Machine Learning approaches with advanced mathematical modeling of the financial markets are evident from the clear improvements that financial operators have obtained in understanding, modeling, and forecasting the assets dynamics (price, trend, etc..). It is estimated that more than 60% of the daily financial transactions of the major investment funds are executed by automatic trading algorithms which analyze in real time the dynamics of such selected financial instruments proceeding to develop trading strategies on the basis of very precise rules obtained from internal statistical inference engines.

This special issue brings together research papers which reports new theoretical or applied algorithms employing mathematical modeling and/or machine learning in a variety of financial issues. We strongly encourage the submission of papers that explore new research perspectives in different areas of quantitative finance including, but not restricted to, forecasting and analysis of financial time series, financial networks, fund investment management, trading systems, Machine Learning for High Frequency Trading systems, Algorithmic trading, financial risk management, innovative mathematical algorithms for portfolio allocation and optimization, bio-inspired mathematical models for asset pricing, bio-inspired trading algorithms, genetic trading systems, etc..

The main purpose of this special issue is to highlight the advantages (in terms of accuracy, robustness, profitability, financial sustainability and efficiency) that recent machine learning approaches and advanced bio-inspired mathematical modeling show in addressing financial problems.

In light of these, the Special Issue is also highly interested in publishing papers where novel bio-inspired approaches are presented for addressing classical financial issues. They include the bio-inspired predictive algorithms; advanced reinforcement learning, evolutionary algorithms, advanced genetic programming, heuristic approaches.

The Special Issue also welcomes replication and/or past published studies in any area of quantitative finance with the foresight that they are re-evaluated using alternative methods.

References:

Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, and Qionghai Dai, Deep Direct Reinforcement Learning for Financial  Signal Representation and Trading, IEEE Transactions On Neural Networks And Learning Systems, 2016, DOI: 10.1109/Tnnls.2016.2522401

Kim, J. H., P. Ji, Significance Testing in Empirical Finance: A Critical Review and Assessment, Journal of Empirical Finance, 2015, 34. 1-14.

Dreżewski, R.; Dziuban, G.; Pająk, K. The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market Hypothesis and Sustainable Development Strategies. Sustainability 2018, 10, 1460.

Rundo, F.; Trenta, F.; Di Stallo, A.L.; Battiato, S. Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System. Computation 2019, 7, 4.

Yelin Li, Junjie Wu and Hui Bu, "When quantitative trading meets machine learning: A pilot survey," 2016 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, 2016, pp. 1-6. doi: 10.1109/ICSSSM.2016.7538632

Li, Bin et al., "PAMR: Passive aggressive mean reversion strategy for portfolio selection", Machine learning, vol. 87.2, pp. 221-258, 2012.

A. N. Akansu, D. Malioutov, D. P. Palomar, E. Jay and D. P. Mandic, "Introduction to the Issue on Financial Signal Processing and Machine Learning for Electronic Trading," in IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 6, pp. 979-981, Sept. 2016. doi: 10.1109/JSTSP.2016.2594458

Brabazon, M. O'Neill,
Biologically Inspired Algorithms for Financial Modelling  Springer Series: Natural Computing, 2006;

Dr. Eng. Francesco Rundo, Ph.D

Guest Editor

 

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