Dear Colleagues,

The Special Issue focuses on business analytics, analytical approaches, and applications. In particular, we aim to publish comprehensive surveys of related existing and emerging trends, opportunities and challenges of scientific and practical significance, their evaluations, and innovative solutions.

Of special interest are contributions that reflect current technological advances (e.g., artificial intelligence, cloud computing, open data, and models), including the consideration of ethics, in particular data security and data privacy, explainability, fairness, etc.

Other suggested topics include:

• Concepts, definitions, theories, models, methods, frameworks, applications, and influencing factors.
• Performance, maturity, capabilities, limitations, etc.
• Readiness, provision, use, and collaboration/cooperation in organizations.
• Impact on business performance and mechanisms for increasing business growth.
• Education, expertise, specializations, and the specialist market.
• Unintentional, (un)responsible, and/or (un)ethical use.

Prof. Dr. Tatiana Ermakova
Prof. Dr. Benjamin Fabian
Guest Editors

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• business analytics
• business application
• analytics
• analytical approach
• artificial intelligence
• business intelligence
• ethics

Title: Interconnected Markets: Unveiling Volatility Spillovers in Commodities and Energy Markets through BEKK GARCH Modeling
Authors: Tetiana Paientko; Stanley Amakude
Affiliation: Business School, Berlin University of Applied Sciences for Economics and Techniques 1
Abstract: .Background: Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred the study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other and to determine how time-varying the spillovers were across a time. Methods: Data was of daily frequency (prices of grains and energy products) from July 1, 2019, to December 31, 2022, as quoted in markets. The choice of the period was to capture the COVID pandemic and the Russian-Ukrainian war as events that could impact volatility. Returns were duly calculated using spreadsheets and subjected to ADF stationarity, Co-integration, and the full-BEKK-GARCH estimation. Results revealed a prolonged association between returns in the energy markets and food commodity market returns. Both markets were found to have volatility persistence individually, and time-varying bi-directional transmission of volatility across the markets was found. No lagged-effects spill-over was found from one market to the other. Conclusions: The findings confirm that shocks that emanate from fluctuations in energy markets are impactful on the volatility of prices in food commodity markets and vice versa, but this impact occurs immediately after the shocks arise, or on the same day such variation occurs.

Title: Directed topic extraction with side information for sustainability analysis
Authors: Maria Osipenko
Affiliation: Hochschule für Wirtschaft und Recht Berlin
Abstract: Topic analysis represents each document of a text corpus in a low dimensional latent topic space. In some cases the desired topic representation is prestructured in form of requirements or guidelines delivering side information. For instance, investors can be interested in automatically assessing sustainability in textual content of corporate reports with a focus on the established 17 UN sustainability goals. The main corpus here contains the corporate report texts, and the texts with the definitions of the 17 UN sustainability goals represent the side information. Under assumption that both text corpora share a common low dimensional subspace, we propose to represent them in a such via directed topic extraction by matrix co-factorization. Both, the main and the side text corpora are first represented as term-document matrices, which are then jointly decomposed into word-topic and topic-document matrices. Thereby, the word-topic matrix is common to both text corpora, whereas the topic-document matrices contain specific representations in the shared topic space. A nuisance parameter, which allows to move focus between error minimization of individual factorization terms, controls the extent, to which the side information is taken into account. With our approach, documents from the main and the side corpora can be related to each other in the resulting latent topic space. That is, the considered corporate reports are represented in the same latent topic space as the descriptions of the 17 UN sustainability goals, such that a structured automatic sustainability assessment of textual reports content is possible. We provide an algorithm for such directed topic extraction and propose techniques for visualizing and interpreting the results.