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 850 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 (2 papers)

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Research

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
[...] Read more.
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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