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Computation
Special Issues
Computational Approaches in Corporate Finance, Risk Management and Financial Markets
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Computational Approaches in Corporate Finance, Risk Management and Financial Markets

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4367

Special Issue Editor

Prof. Dr. Ştefan Cristian Gherghina

E-Mail Website
Guest Editor
Department of Finance, Faculty of Finance and Banking, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: corporate finance; corporate governance; quantitative finance; sustainable development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the application of computational approaches in the fields of corporate finance, risk management, and financial markets. The integration of computational methods offers novel opportunities to analyze complex financial systems, assess related risks, optimize decision-making processes, and address the major challenges corporations face today. We invite submissions that employ computational techniques, such as machine learning, data mining, network analysis, simulation, and optimization, to advance the knowledge and understanding in these interconnected fields. Topics of interest include, but are not limited to, the following:

  • Assessing investment decisions in equity crowdfunding;
  • Developing early warning models against bankruptcy risk;
  • Artificial neural networks for corporate distress modelling;
  • Multi-criteria decision-making methods towards risk assessment;
  • Deep learning for portfolio optimization;
  • Copula approaches towards measuring financial contagion;
  • Volatility forecasting in the cryptocurrency markets;
  • Sentiment analysis of financial news;
  • Predicting stock prices using machine learning;
  • Exploring stock market volatility connectedness.

Prof. Dr. Ştefan Cristian Gherghina
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 submissions that pass pre-check are 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. Computation 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 1800 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

  • deep learning
  • early warning models
  • artificial neural networks
  • multi-criteria decision-making methods
  • volatility forecasts
  • risk spillover
  • copulas
  • minimum spanning tree
  • transfer entropy
  • wavelet coherence analysis

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

40 pages, 773 KiB  
Article
Multiple Behavioral Conditions of the Forward Exchange Rates and Stock Market Return in the South Asian Stock Markets During COVID-19: A Novel MT-QARDL Approach
by Mosab I. Tabash, Adel Ahmed, Suzan Sameer Issa, Marwan Mansour, Manishkumar Varma and Mujeeb Saif Mohsen Al-Absy
Computation 2024, 12(12), 233; https://doi.org/10.3390/computation12120233 - 26 Nov 2024
Viewed by 458
Abstract
This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) model, and the Multiple Threshold Non-linear Autoregressive [...] Read more.
This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) model, and the Multiple Threshold Non-linear Autoregressive Distributive Lag (NARDL) model propose a new Multiple Threshold Quantile Autoregressive Distributive Lag (MT-QARDL) approach. Unlike MT-NARDL, QARDL, and NARDL, the MT-QARDL model, which integrates the MT-NARDL model and the quantile regression methodology, captures both short- and long-term locational and sign-based asymmetries. For instance, at lower quantiles for Indian and Sri Lankan SMRs, bearish FERP exerts a positive influence, while bullish FERP has a negative effect during COVID-19. Conversely, bullish FERP negatively affects lower quantiles of SMRs of Bangladesh, India, and Sri Lanka, whereas bearish FERP either yields an opposite effect or remain statistically insignificant during COVID-19. The findings underscore long-term sign-based asymmetries due to the differential bearish and bullish FERP impact during COVID-19. However, in the long term, location-based asymmetries also existed as bullish FERP negative influence the SMRs of India, Bangladesh and Sri Lanka at higher quantiles but SMRs at lower quantiles insignificantly respond to the bullish FERP fluctuations during COVID-19. Full article
(This article belongs to the Special Issue Computational Approaches in Corporate Finance, Risk Management and Financial Markets)
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18 pages, 1234 KiB  
Article
Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production
by Dadiana Dabija, Carmen-Eugenia Nastase, Ancuţa Chetrariu and Adriana Dabija
Computation 2024, 12(10), 206; https://doi.org/10.3390/computation12100206 - 15 Oct 2024
Viewed by 1291
Abstract
As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk [...] Read more.
As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices. Full article
(This article belongs to the Special Issue Computational Approaches in Corporate Finance, Risk Management and Financial Markets)
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24 pages, 10127 KiB  
Article
Unveiling AI-Generated Financial Text: A Computational Approach Using Natural Language Processing and Generative Artificial Intelligence
by Muhammad Asad Arshed, Ștefan Cristian Gherghina, Christine Dewi, Asma Iqbal and Shahzad Mumtaz
Computation 2024, 12(5), 101; https://doi.org/10.3390/computation12050101 - 15 May 2024
Viewed by 1718
Abstract
This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this [...] Read more.
This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this research pioneers the identification of financial text derived from AI models such as ChatGPT and paraphrasing tools like QuillBot. While our primary focus is on financial content, we have also pinpointed texts generated by paragraph rewriting tools and utilized ChatGPT for various contexts this multiclass identification was missing in previous studies. In this paper, we use a comprehensive feature extraction methodology that combines TF–IDF with Word2Vec, along with individual feature extraction methods. Importantly, combining a Random Forest model with Word2Vec results in impressive outcomes. Moreover, this study investigates the significance of the window size parameters in the Word2Vec approach, revealing that a window size of one produces outstanding scores across various metrics, including accuracy, precision, recall and the F1 measure, all reaching a notable value of 0.74. In addition to this, our developed model performs well in classification, attaining AUC values of 0.94 for the ‘GPT’ class; 0.77 for the ‘Quil’ class; and 0.89 for the ‘Real’ class. We also achieved an accuracy of 0.72, precision of 0.71, recall of 0.72, and F1 of 0.71 for our extended prepared dataset. This study contributes significantly to the evolving landscape of AI text identification, providing valuable insights and promising directions for future research. Full article
(This article belongs to the Special Issue Computational Approaches in Corporate Finance, Risk Management and Financial Markets)
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