Special Issue "Algorithms in Computational Finance"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 September 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Edward Tsang

School of Computer Science and Electronic Engineering, the University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
Website | E-Mail
Interests: artificial intelligence; computational finance
Guest Editor
Dr. V L Raju Chinthalapati

Department of Accounting & Finance, Business School, The University of Greenwich, London SE10 9LS, UK
Website | E-Mail
Interests: quantitative and computational finance; financial econometrics; big data analytics; agent-based models; operations research

Special Issue Information

Dear Colleagues,

Algorithms play an important part in finance. Financial markets have transformed from human-driven systems to predominantly computer driven. This transformation has laid the foundation for a new breed of trader; the algorithm. Algorithms are used for forecasting, decision making and trading in financial markets. Algorithmic trading is a hot topic in finance as over 70% of trades by volume is generated by trading programs. Algorithms are also used to analyse data for detecting changes in the market. Such analysis are useful to traders and fund managers who may want to detect trading opportunities and manage risk. Analysis is also important to regulators who may want to build early warning systems to protect the economy.

This Special Issue aims to attract submissions that report state-of-the-art research in algorithms in computational finance. The scope of this Special Issue is broad. We welcome submission in, but not limited to, the following topics:

  • Forecasting algorithms
  • Trading algorithms
  • Portfolio optimisation algorithms
  • Algorithms for analysing financial data
  • Algorithms for market analysis, e.g., for early warning systems
  • Machine learning applications in finance

Prof. Dr. Edward Tsang
Dr. V L Raju Chinthalapati
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

Open AccessArticle The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach
Algorithms 2019, 12(1), 1; https://doi.org/10.3390/a12010001
Received: 16 November 2018 / Revised: 8 December 2018 / Accepted: 17 December 2018 / Published: 20 December 2018
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Abstract
An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated
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An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Classification of Normal and Abnormal Regimes in Financial Markets
Algorithms 2018, 11(12), 202; https://doi.org/10.3390/a11120202
Received: 14 September 2018 / Revised: 23 November 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
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Abstract
When financial market conditions change, traders adopt different strategies. The traders’ collective behaviour may cause significant changes in the statistical properties of price movements. When this happens, the market is said to have gone through “regime changes”. The purpose of this paper is
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When financial market conditions change, traders adopt different strategies. The traders’ collective behaviour may cause significant changes in the statistical properties of price movements. When this happens, the market is said to have gone through “regime changes”. The purpose of this paper is to characterise what is a “normal market regime” as well as what is an “abnormal market regime”, under observations in Directional Changes (DC). Our study starts with historical data from 10 financial markets. For each market, we focus on a period of time in which significant events could have triggered regime changes. The observations of regime changes in these markets are then positioned in a designed two-dimensional indicator space based on DC. Our results suggest that the normal regimes from different markets share similar statistical characteristics. In other words, with our observations, it is possible to distinguish normal regimes from abnormal regimes. This is significant, because, for the first time, we can tell whether a market is in a normal regime by observing the DC indicators in the market. This opens the door for future work to be able to dynamically monitor the market for regime change. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle An Algorithmic Look at Financial Volatility
Algorithms 2018, 11(11), 185; https://doi.org/10.3390/a11110185
Received: 13 September 2018 / Revised: 29 October 2018 / Accepted: 4 November 2018 / Published: 13 November 2018
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Abstract
In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once
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In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once transformed into equally proportional binary strings. Frequency ranking of binary trading weeks coincides with that of their Kolmogorov complexity estimated by Delahaye et al. (2012). According to Levin’s universal distribution, large (resp. small) volatilities are more likely to be followed by large (resp. small) ones since simple trading weeks such as “00000” or “11111” are much more frequently observed than complex ones such as “10100” or “01011”. Thus, volatility clusters may not be attributed to behavioral or micro-structural assumptions but to the complexity discrepancy between finite strings. This property of financial data could be at the origin of volatility autocorrelation, though autocorrelated volatilities simulated from Generalized Auto-Regressive Conditional Heteroskedacity (hereafter GARCH) cannot be transformed into universally distributed binary weeks. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
Algorithms 2018, 11(11), 171; https://doi.org/10.3390/a11110171
Received: 17 September 2018 / Revised: 22 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018
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Abstract
The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on
[...] Read more.
The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on the DC framework. Despite the promising results of DBA, DBA employed neither an order size management nor risk management components. In this paper, we present an improved version of DBA named Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules. We examine the performance of IDBA in the forex market. The results suggest that IDBA can provide significantly greater returns than DBA. The results also show that the IDBA outperforms another DC-based trading strategy and that it can generate annualized returns of about 30% after deducting the bid and ask spread (but not the transaction costs). Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle A Machine Learning View on Momentum and Reversal Trading
Algorithms 2018, 11(11), 170; https://doi.org/10.3390/a11110170
Received: 15 September 2018 / Revised: 18 October 2018 / Accepted: 24 October 2018 / Published: 26 October 2018
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Abstract
Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it
[...] Read more.
Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Chronotype, Risk and Time Preferences, and Financial Behaviour
Algorithms 2018, 11(10), 153; https://doi.org/10.3390/a11100153
Received: 14 September 2018 / Revised: 5 October 2018 / Accepted: 8 October 2018 / Published: 10 October 2018
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Abstract
This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results
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This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results reveal that morningness is negatively associated with delinquent credit card payments. Morningness also indirectly predicts delinquent credit card payments through time preference, but this relationship only exists when individuals’ monthly income is at a low and average level. On the other hand, financial risk preference accounts for the effect of morningness on stock market participation. Consequently, an additional finding is that morningness is positively associated with financial risk preference, which contradicts previous findings in the literature. Finally, based on the empirical evidence, we discuss the plausible mechanisms that may drive these relationships and the implications for theory and practice. The current study contributes to the literature by examining the links between circadian typology and particular financial behaviour of experienced workers. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction
Algorithms 2018, 11(9), 138; https://doi.org/10.3390/a11090138
Received: 30 April 2018 / Revised: 2 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
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Abstract
We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly
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We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficiency. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P 500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization
Algorithms 2018, 11(5), 72; https://doi.org/10.3390/a11050072
Received: 11 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
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Abstract
In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does
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In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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