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1 – 10 of 14Murat Ertuğrul and Mustafa Hakan Saldi
The study is called for to eliminate the noise between the significant macro variables from the perspective of the cause-and-effect approach to indicate why and how the return of…
Abstract
Introduction
The study is called for to eliminate the noise between the significant macro variables from the perspective of the cause-and-effect approach to indicate why and how the return of solar projects is being affected by these.
Purpose
The study aims to investigate the spread between unit selling electricity prices of a monthly production of 250 KW solar project installed in Türkiye and USD/TRY.
Methodology
A relational framework is designed by drawing on the variables determined as crude oil prices, United States (US) 2-year yield, Dollar Index (DXY), USD/TRY, the annual inflation rate of Türkiye, and unit selling electricity prices. Then, a multivariate approach is performed through Matlab to analyse the correlational relationships and structure the curve estimation models.
Findings
The observations show that the gradually rising spread between unit selling electricity price and USD/TRY signals the reduction in return-on-investment rate of solar energy projects because of the particular causes of the European energy crisis by the reason of Russia and Ukraine war and escalating risks in DXY and US treasury yields as a result of federal fund rate hikes against inflationary pressures. Solar energy investments are delicate instruments to global oil shocks and higher DXY in controlling Inflation and currency volatility; therefore, resilient policies should solicit the demand because of environmental and economic reasons to reduce the external dependency of Türkiye.
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Elena G. Popkova, Tatiana N. Litvinova and Olga M. Zemskova
The research focuses on the problem of the mismatch between the current approach to corporate accounting and reporting according to International Financial Reporting Standards…
Abstract
The research focuses on the problem of the mismatch between the current approach to corporate accounting and reporting according to International Financial Reporting Standards (IFRS) and the new paradigm of international entrepreneurship development in Russia. The research aims to identify global prerequisites and prospects for improving the approach to corporate accounting and reporting of international entrepreneurship according to IFRS in Russia. Drawing on international experience in developing digital economies from 2021 to 2023, the authors apply regression analysis to create an econometric model of IFRS application in international entrepreneurship. The model determines patterns of changes in the investment attractiveness of the economy for foreign investments as corporate accounting and reporting in international entrepreneurship are automated using big data, smart analytics, and other digital technologies. The main authors' conclusion is that smart automation of corporate accounting and reporting in international entrepreneurship according to IFRS ensures an influx of foreign investments. The theoretical significance lies in developing a new approach to corporate accounting and reporting for international entrepreneurship according to IFRS in Russia. The practical significance is expressed in the perspective offered to enhance the attractiveness of Russia's economy for foreign investments through smart automation of corporate accounting and reporting for international entrepreneurship according to IFRS. This can be utilized, first, in the business practices of international entrepreneurship in Russia to increase investment attractiveness for foreign investors. Second, it can be utilized in the state economic policy to stimulate the influx of foreign investments into the digitalization of Russia's economy.
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Md Badrul Alam, Muhammad Tahir and Norulazidah Omar Ali
This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in…
Abstract
Purpose
This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature.
Design/methodology/approach
To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models.
Findings
The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable.
Practical implications
The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows.
Originality/value
The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context.
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Valeriia Baklanova, Aleksei Kurkin and Tamara Teplova
The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the…
Abstract
Purpose
The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.
Design/methodology/approach
The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.
Findings
The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.
Practical implications
The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.
Originality/value
The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.
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Marko Kureljusic and Erik Karger
Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current…
Abstract
Purpose
Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.
Design/methodology/approach
The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.
Findings
The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.
Research limitations/implications
Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.
Practical implications
Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.
Originality/value
To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.
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Serdar Simonyan and Sema Bayraktar
This paper examines the relationship between sovereign credit default swaps (CDS) and several macroeconomic factors in an asymmetric setting and distinguishes between short-run…
Abstract
Purpose
This paper examines the relationship between sovereign credit default swaps (CDS) and several macroeconomic factors in an asymmetric setting and distinguishes between short-run and long-run impacts. Country-specific factors (e.g. equity index, international reserves, interest rate and industrial production) and global factors (e.g. US stock volatility [VIX], geopolitical risk and oil price) are the main explanatory variables.
Design/methodology/approach
This analysis uses a nonlinear autoregressive distributed lag approach that enables us to study both long-run and short-run dynamics.
Findings
This study results show that two country-specific factors (equity index and international reserves) and two global factors (VIX and oil price) are the most important factors and affect CDS asymmetrically.
Research limitations/implications
The asymmetric relationships between sovereign CDS and variables in bull and bear markets can also be studied. Consideration of asymmetries in the variance could also be a fruitful step taken for further research.
Practical implications
The findings imply that investors and portfolio managers should design their investment and hedging decisions related to government bonds by taking into account the existence of an asymmetric relationship.
Social implications
Moreover, policymakers can benefit from this asymmetric information in the timing of debt issuance.
Originality/value
This paper examines the relationship between sovereign CDS and several macroeconomic factors in an asymmetric setting and distinguishes between short-run and long-run impacts.
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Bayu Arie Fianto, Syed Alamdar Ali Shah and Raditya Sukmana
This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.
Abstract
Purpose
This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.
Design/methodology/approach
This study uses a quantile bounded autoregressive distributed lag (QBARDL) model to uncover relevant relationships.
Findings
This study finds that the Dow Jones Islamic Market Index, gold returns, world oil prices and exchange rates are the determinants of the Indonesia’s Islamic stock returns. However, the relationship is time varying developing intra-/inter-quantile bounded.
Practical implications
Integration of the Islamic stock returns with the real economic indicators changes over time. The findings have important implications for the policymakers, the fund managers and the investors to anticipate consequences when considering the macroeconomic conditions before participating in the Indonesian Islamic stock market.
Originality/value
Using a QBARDL, this study finds that the Islamic stock returns have on net and “time-varying intra-/inter-quantile developing” relationship with its determinants as data quantiles progressed from 25% to 75%.
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Diego Silveira Pacheco de Oliveira and Gabriel Caldas Montes
Given the importance of credit rating agencies’ (CRAs) assessment in affecting international financial markets, it is useful for policymakers and investors to be able to forecast…
Abstract
Purpose
Given the importance of credit rating agencies’ (CRAs) assessment in affecting international financial markets, it is useful for policymakers and investors to be able to forecast it properly. Therefore, this study aims to forecast sovereign risk perception of the main agencies related to Brazilian bonds through the application of different machine learning (ML) techniques and evaluate their predictive accuracy in order to find out which one is best for this task.
Design/methodology/approach
Based on monthly data from January 1996 to November 2018, we perform different forecast analyses using the K-Nearest Neighbors, the Gradient Boosted Random Trees and the Multilayer Perceptron methods.
Findings
The results of this study suggest the Multilayer Perceptron technique is the most reliable one. Its predictive accuracy is relatively high if compared to the other two methods. Its forecast errors are the lowest in both the out-of-sample and in-sample forecasts’ exercises. These results hold if we consider the CRAs classification structure as linear or logarithmic. Moreover, its forecast errors are not statistically associated with periods of changes in CRAs’ opinion of any sort.
Originality/value
To the best of the authors’ knowledge, this study is the first to evaluate the performance of ML methods in the task of predicting sovereign credit news, including not only the sovereign ratings but also the outlook and credit watch status. In addition, the authors investigate whether the forecasts errors are statistically associated with periods of changes in sovereign risk perception.
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Deske W. Mandagi and Dave Centeno
Anchored in the theories of brand gestalt and stakeholder perspectives, this study aims to undertake a comprehensive examination of the brand gestalt concept, emphasizing its…
Abstract
Purpose
Anchored in the theories of brand gestalt and stakeholder perspectives, this study aims to undertake a comprehensive examination of the brand gestalt concept, emphasizing its multidimensional nature and the process of co-creation.
Design/methodology/approach
Focused within the context of the Wonderful Indonesia brand, the research draws upon a rich qualitative data set derived from in-depth interviews conducted with 18 international tourists, supplemented by netnography (or internet ethnography) of websites, social media and online articles related to Wonderful Indonesia. Using grounded theory methodology, the qualitative data undergo rigorous analysis to identify emergent themes and patterns.
Findings
The research elucidates the four dimensions (4S) comprising brand gestalt: storyscapes, sensescapes, servicescapes and stakeholderscapes. Each dimension is further delineated into essential categories, providing a comprehensive understanding of brand gestalt. This study highlights the collaborative nature of brand gestalt, emphasizing the involvement of multiple stakeholders in shaping the brand's identity and perception. Consumer perceptions of co-creation are identified as significant contributors to brand gestalt, enhancing the brand's value proposition.
Practical implications
Destination management and practitioners can use the insights from the research to refine their brand management and marketing strategies by leveraging the dimensions of brand gestalt. Recognizing the collaborative construct of brand gestalt can guide businesses in fostering meaningful relationships with stakeholders and aligning branding efforts with collective visions. Understanding the role of consumer co-creation in brand development can inform strategies aimed at enhancing brand equity and fostering consumer loyalty.
Originality/value
This study extends existing literature on brand gestalt by providing a comprehensive examination of its four dimensions and essential categories. By emphasizing the collaborative nature of brand gestalt, this study contributes to advancing the understanding of brand co-creation paradigms. The identification of consumer perceptions of co-creation as a significant factor in brand gestalt adds novel insights to the literature, offering valuable implications for brand management and marketing strategies.
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Marcos Dieste, Guido Orzes, Giovanna Culot, Marco Sartor and Guido Nassimbeni
A positive outlook on the impact of Industry 4.0 (I4.0) on sustainability prevails in the literature. However, some studies have highlighted potential areas of concern that have…
Abstract
Purpose
A positive outlook on the impact of Industry 4.0 (I4.0) on sustainability prevails in the literature. However, some studies have highlighted potential areas of concern that have not yet been systematically addressed. The goal of this study is to challenge the assumption of a sustainable Fourth Industrial Revolution by (1) identifying the possible unintended negative impacts of I4.0 technologies on sustainability; (2) highlighting the underlying motivations and potential actions to mitigate such impacts; and (3) developing and evaluating alternative assumptions on the impacts of I4.0 technologies on sustainability.
Design/methodology/approach
Building on a problematization approach, a systematic literature review was conducted to develop potential alternative assumptions about the negative impacts of I4.0 on sustainability. Then, a Delphi study was carried out with 43 experts from academia and practice to evaluate the alternative assumptions. Two rounds of data collection were performed until reaching the convergence or stability of the responses.
Findings
The results highlight various unintended negative effects on environmental and social aspects that challenge the literature. The reasons behind the high/low probability of occurrence, the severity of each impact in the next five years and corrective actions are also identified. Unintended negative environmental effects are less controversial than social effects and are therefore more likely to generate widely accepted theoretical propositions. Finally, the alternative hypothesis ground is partially accepted by the panel, indicating that the problematization process has effectively opened up new perspectives for analysis.
Originality/value
This study is one of the few to systematically problematize the assumptions of the I4.0 and sustainability literature, generating research propositions that reveal several avenues for future research.
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