Special Issue "Artificial Neural Networks in Business"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Jakub Horak
E-Mail Website
Guest Editor
School of Expertness and Valuation, Institute of Technology and Business in Ceske Budejovice, 37001 Ceske Budejovice, Czech Republic
Interests: neural networks in business; corporate finance; financial analysis; comprehensive evaluation methods; business value; quantitative methods in economy; financial time series prediction

Special Issue Information

Dear Colleagues,

Artificial neural networks are currently being used in many areas, including those responsible for human life or connected with significant financial impact. This Special Issue is therefore looking for papers addressing issues related to their applicability and reliability. These networks also play an important role in business and are an important part of various business activities.  This Special Issue is therefore focused on the possibilities of artificial neural networks, the current status of technology, and the applicability of artificial neural networks in current business practice.

Theoretical and empirical papers focused on the application of artificial neural networks for regression, classification or cluster analysis in a company are therefore solicited.  Papers dealing with the application of artificial networks within the evaluation and prediction of time series in business, and that support decision-making processes in companies, the creation of creditworthiness and bankruptcy models, or process management in companies are also welcome.

Papers aimed at the application of neural networks in the consumers´ responses to new products, detection of credit risk, detection of machine and computer failures, product quality recognition, prediction of security prices, demand and sales prediction, cost or inventory management, determination of value generators, financial plan and similar applications are especially welcome.

Dr. Jakub Horak
Guest Editor

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. Journal of Risk and Financial Management 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 1200 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.

Keywords

  • neural networks 
  • business finance 
  • predictions 
  • securities 
  • time series 
  • inventory management

Published Papers (4 papers)

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Research

Article
A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework
J. Risk Financial Manag. 2021, 14(6), 254; https://doi.org/10.3390/jrfm14060254 - 07 Jun 2021
Viewed by 239
Abstract
In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a [...] Read more.
In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Business)
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Article
Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China
J. Risk Financial Manag. 2021, 14(2), 76; https://doi.org/10.3390/jrfm14020076 - 10 Feb 2021
Viewed by 472
Abstract
The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth [...] Read more.
The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth in the CR’s exports to the PRC can be expected and whether MLP (Multi-Layer Perceptron) networks are applicable for predicting the future development of the CR’s exports to the PRC. On the basis of previously obtained historical data, ANN with the best explanatory power are generated. For the purpose specified, three experiments are carried out, the results of which are described in detail. For the first, second and third experiments, ANN for predicting the development of exports are generated on the basis of a time series with a 1-month, 5-month and 10-month time delay, respectively. The generated ANN are the MLP and regression time series neural networks. The MLP turn out to be the most efficient in predicting the future development of the CR’s exports to the PRC. They are also able to predict possible extremes. It is also determined that the USA–China trade war has significantly affected the CR’s exports to the PRC. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Business)
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Article
Sanctions as a Catalyst for Russia’s and China’s Balance of Trade: Business Opportunity
J. Risk Financial Manag. 2021, 14(1), 36; https://doi.org/10.3390/jrfm14010036 - 14 Jan 2021
Viewed by 539
Abstract
Economic sanctions are among the most powerful instruments of international policy. However, this study, using the example of the so-called anti-Russian sanctions, shows that in the global economy, countries are rapidly using other alternatives, and sanctions in the case analyzed act as a [...] Read more.
Economic sanctions are among the most powerful instruments of international policy. However, this study, using the example of the so-called anti-Russian sanctions, shows that in the global economy, countries are rapidly using other alternatives, and sanctions in the case analyzed act as a catalyst for balance of trade between the Russian Federation and the People’s Republic of China. The study is based on a highly topical sophisticated model of neural networks, which provides clear results confirming the unintended positive effect. The time series and aggregated data became inputs into multilayer perceptron networks, while the methodology used enabled eliminating of both too large averaging and extreme fluctuations of the equalized time series. Out of 10,000 networks created for each variable and each time lag, five showing the best characteristics given by correlation coefficients and absolute residual sums were retained. Thus, the created equalized time series were able to describe the basic trend of the actual development of export and import, while also capturing their local extremes. The interpolation of the two time series shows that the sanctions imposed on the Russian Federation in 2014 have clearly strengthened its balance of trade with the People’s Republic of China. The results of the study also predict further growth in the balance of trade between the Russian Federation and the People’s Republic of China, although this development may be delayed by current events. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Business)
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Article
Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach
J. Risk Financial Manag. 2020, 13(11), 285; https://doi.org/10.3390/jrfm13110285 - 17 Nov 2020
Viewed by 748
Abstract
Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their [...] Read more.
Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Business)
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