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Special Issue "Advances in Predictive Analytics and Machine Learning"

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: 20 May 2023 | Viewed by 793

Special Issue Editors

Prof. Dr. Kevin Dow
E-Mail Website
Guest Editor
Department of Accounting and Information Systems, University of Texas at El Paso, El Paso, TX 79968, USA
Interests: machine learning; predictive analytics; accounting information systems
Dr. Dulani Jayasuriya
E-Mail
Guest Editor
Department of Accounting and Finance, University of Auckland, 1142 Auckland, Australia
Interests: banking; machine learning; blockchain and cryptocurrencies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Predictive analytics is presently a very active research area with a large scope with regard to applications and theory. Machine learning in recent years has become one of its core foundation pillars. Predictive analytics in accounting and finance provide key challenges that can be addressed with machine learning techniques and methodologies. This Special Issue focuses on predictive analytics in accounting and finance and the latest developments of machine learning in these areas. Moreover, we focus on the synergy between machine learning and predictive analytics within the context of accounting and financial management. We welcome new developments in predictive analytics and machine learning that is relevant to accounting and finance, including foundations and innovative applications. Theoretically and empirically well-founded contributions and their real-world applications that provide new foundations in predictive analytics and machine learning are welcome.

Research is needed to understand and improve the potential and suitability of predictive analytics and machine learning in the context of financial management services. This will provide a much deeper understanding and better decision making based on data. It will also present opportunities for improving machine learning and predictive analytics algorithms and methods on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in risk and financial management.

This Special Issue solicits a broad research audience, combines numerous real-world best practices and foundational issues, and is relevant to industry practitioners interested in predictive analytics and machine learning. Contributions must be new, unpublished, original, and fundamental work. All submissions will be blind peer-reviewed using scientific criteria.

We welcome original research papers on all aspects of predictive analytics and machine learning, including the following topics:

Foundations of Machine Learning in Predictive Analytics

  • Feature engineering and data preprocessing
  • Reinforcement learning
  • Risk analysis
  • Causality, learning casual models
  • Semi-supervised and weakly supervised learning
  • Data streaming and online learning
  • Deep learning
  • Visual analytics
  • Big data analytics governance
  • Big data implications in enterprise models and practices
  • Big data and machine learning impacts on knowledge management
  • Knowledge development, discovery, and decision making from big data in financial management
  • Data mining, statistical modeling, and machine learning for financial management
  • How big data analytics can enable accounting and financial management services
  • Novel approaches to risk and financial management based on data analytics and machine learning
  • Leveraging data analytics in accounting and financial management
  • The future of predictive analytics and machine learning in accounting and financial management
  • Creating organizational value from predictive analytics and machine learning
  • IoT applications in accounting and financial management

Predictive Analytics for the Next Digital Era

  • The impact of blockchains, cryptocurrencies on accounting and finance
  • Climate change and sustainable environment.

Prof. Dr. Kevin Dow
Dr. Dulani Jayasuriya
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 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. 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

  • predictive analytics
  • machine learning
  • causal models

Published Papers (2 papers)

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Research

Article
Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning
J. Risk Financial Manag. 2022, 15(12), 552; https://doi.org/10.3390/jrfm15120552 - 24 Nov 2022
Viewed by 265
Abstract
Movements in the India VIX are an important gauge of how the market’s risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period [...] Read more.
Movements in the India VIX are an important gauge of how the market’s risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period of study is from April 2009 to March 2021. To achieve the stated task, classifiers were trained and validated with 90% of the given sample, considering two-fold time-series cross-validation for hyper-tuning. Optimised models were then predicted on an unseen test dataset, representing 10% of the given sample. The results showed that optimal models performed well, and their accuracy scores were similar, with minor variations ranging from 63.33% to 67.67%. The stacking classifier achieved the highest accuracy. Furthermore, CatBoost, Light Gradient Boosted Machine (LightGBM), Extreme Gradient Boosting (XGBoost), voting, stacking, bagging and Random Forest classifiers are the best models with statistically similar performances. Among them, CatBoost, LightGBM, XGBoost and Random Forest classifiers can be recommended for forecasting day-to-day movements of the India VIX because of their inherently optimised structure. This finding is very useful for anticipating risk in the Indian stock market. Full article
(This article belongs to the Special Issue Advances in Predictive Analytics and Machine Learning)
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Article
Religiosity at the Top and Annual Report Readability
J. Risk Financial Manag. 2022, 15(10), 485; https://doi.org/10.3390/jrfm15100485 - 21 Oct 2022
Viewed by 419
Abstract
This paper examines how individual religiosity at the top level of organizations affects the quality of their disclosure practices, as measured by the readability of annual reports. Our paper extends the recent accounting and finance literature that moves away from a location-based measure [...] Read more.
This paper examines how individual religiosity at the top level of organizations affects the quality of their disclosure practices, as measured by the readability of annual reports. Our paper extends the recent accounting and finance literature that moves away from a location-based measure to an individual-based measure for capturing the effect of religiosity. Our findings suggest that the individual religiosity of C-suite executives matters in corporate decision-making and has positive implications for the quality of corporate disclosure practices, as reflected by more readable reports. This main finding is primarily driven by the religiosity of CEOs. Additional findings also suggest that the effect of religiosity is not solely driven by the religious denomination of the majority group within a given location-based setting. Previous research using religiosity proxies based on the majority religion in the locale of firms’ headquarters may have measurement issues that disguise the effect of religiosity. This issue is particularly problematic when CEOs or other executives participate in minority religious denominations. Overall, our paper finds that CEO religiosity is an important attribute that affects the overall quality of business practice. Full article
(This article belongs to the Special Issue Advances in Predictive Analytics and Machine Learning)
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