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

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2020) | Viewed by 43817

Special Issue Editor

Prof. Dr. Francesco Rundo
E-Mail Website
Guest Editor
STMicroelectronics, ADG R&D Power and Discretes Division, Artificial Intelligence Team, Catania, Italy
Interests: deep learning systems; explainable deep learning for automotive and healthcare applications; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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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Keywords

  • Machine Learning algorithms for Trading System;
  • Machine Learning algorithms for Quantitative Finance Applications;
  • Bio-Inspired Mathematical Algorithms for Trading Systems;
  • Advanced Algorithms for High Frequency Trading Systems;
  • Intraday Trading System strategies;
  • Deep learning for adaptive trading systems;
  • Reinforcement Learning for Adaptive Trading Systems;
  • Advanced Strategies for Portfolio/Asset Allocation;
  • Advanced Strategies for Portfolio/Asset Optimization;
  • Machine Learning for complex financial instruments;
  • Bio-inspired strategies for profitable investment;
  • Machine Learning for Price Action Trading systems;
  • Mathematical models for news technical financial indicators;
  • Advances in Financial Time-series forecasting;
  • Machine Learning for Financial Time-series forecasting;

Published Papers (6 papers)

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Research

Article
Importance of Event Binary Features in Stock Price Prediction
Appl. Sci. 2020, 10(5), 1597; https://doi.org/10.3390/app10051597 - 28 Feb 2020
Cited by 5 | Viewed by 1160
Abstract
In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has [...] Read more.
In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction. Full article
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Article
Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading
Appl. Sci. 2020, 10(2), 437; https://doi.org/10.3390/app10020437 - 07 Jan 2020
Cited by 29 | Viewed by 4051
Abstract
In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the [...] Read more.
In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on analyzing financial historical data based on statistical techniques, which is known as a type of time series analysis with limited achievements. Recently, deep learning techniques, specifically recurrent neural network (RNN), has been designed to work with sequence prediction. In this paper, a long short-term memory (LSTM) network, which is a special kind of RNN, is proposed to predict stock movement based on historical data. In order to construct an efficient portfolio, multiple portfolio optimization techniques, including equal-weighted modeling (EQ), simulation modeling Monte Carlo simulation (MCS), and optimization modeling mean variant optimization (MVO), are used to improve the portfolio performance. The results showed that our proposed LSTM prediction model works efficiently by obtaining high accuracy from stock prediction. The constructed portfolios based on the LSTM prediction model outperformed other constructed portfolios-based prediction models such as linear regression and support vector machine. In addition, optimization techniques showed a significant improvement in the return and Sharpe ratio of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Standard and Poor 500 (S&P 500) index in both active returns and Sharpe ratios. Full article
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Article
Machine Learning for Quantitative Finance Applications: A Survey
Appl. Sci. 2019, 9(24), 5574; https://doi.org/10.3390/app9245574 - 17 Dec 2019
Cited by 31 | Viewed by 6161
Abstract
The analysis of financial data represents a challenge that researchers had to deal with. The rethinking of the basis of financial markets has led to an urgent demand for developing innovative models to understand financial assets. In the past few decades, researchers have [...] Read more.
The analysis of financial data represents a challenge that researchers had to deal with. The rethinking of the basis of financial markets has led to an urgent demand for developing innovative models to understand financial assets. In the past few decades, researchers have proposed several systems based on traditional approaches, such as autoregressive integrated moving average (ARIMA) and the exponential smoothing model, in order to devise an accurate data representation. Despite their efficacy, the existing works face some drawbacks due to poor performance when managing a large amount of data with intrinsic complexity, high dimensionality and casual dynamicity. Furthermore, these approaches are not suitable for understanding hidden relationships (dependencies) between data. This paper proposes a review of some of the most significant works providing an exhaustive overview of recent machine learning (ML) techniques in the field of quantitative finance showing that these methods outperform traditional approaches. Finally, the paper also presents comparative studies about the effectiveness of several ML-based systems. Full article
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Article
Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems
Appl. Sci. 2019, 9(20), 4460; https://doi.org/10.3390/app9204460 - 21 Oct 2019
Cited by 24 | Viewed by 4978
Abstract
High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so [...] Read more.
High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability. Full article
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Article
A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
Appl. Sci. 2019, 9(17), 3509; https://doi.org/10.3390/app9173509 - 26 Aug 2019
Cited by 25 | Viewed by 21318
Abstract
This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of [...] Read more.
This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data. Full article
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Article
Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for Trading FX Market
Appl. Sci. 2019, 9(9), 1796; https://doi.org/10.3390/app9091796 - 29 Apr 2019
Cited by 23 | Viewed by 5317
Abstract
Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning [...] Read more.
Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For these reasons, grid trading is certainly one of the best approaches to be used in high frequency trading (HFT) strategies. Due to the high level of unpredictability of the financial markets, many investment funds and institutional traders are opting for the HFT (high frequency trading) systems, which allow them to obtain high performance due to the large number of financial transactions executed in the short-term timeframe. The combination of HFT strategies with the use of machine learning methods for the financial time series forecast, has significantly improved the capability and overall performance of the modern automated trading systems. Taking this into account, the authors propose an automatic HFT grid trading system that operates in the FOREX (foreign exchange) market. The performance of the proposed algorithm together with the reduced drawdown confirmed the effectiveness and robustness of the proposed approach. Full article
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