Borsa Istanbul Review最新文献

The possible effect of the financialization of agricultural commodities on food security has become an evolving worry in recent years. This study seeks to empirically investigate this complicated problem and influence policy choices to ensure a more stable and secure food system by analyzing the role of financialization in global food markets. The study uses the panel data regression model, moderating effects model, and panel data regression with threshold variable to analyze financialization due to three agricultural commodities: wheat, maize, and soybean. For wheat, maize, and soybean futures traded on the Chicago Board of Trade, we utilize data related to annual trading volume, annual open interest contracts, and a ratio of annual trading volume to annual open interest contracts. The sample covers five developed countries - the United States, Australia, Canada, France, and Germany, and seven developing countries- China, Russia, India, Indonesia, Brazil, Vietnam, and Thailand. Annual panel data are constructed for the 2000–2021 period. The Human Development Index (HDI) is the threshold variable to differentiate the impact across these countries. The findings reveal that the financialization of agricultural commodities has negatively impacted food security globally, with wheat and soybean having a greater negative impact than corn. Also, there is a more considerable impact on developing countries compared to developed countries. The study finds that monetary policy can potentially reduce the impact of agricultural financialization on food security. The findings of this paper act as a guide to assist policymakers in ensuring that the world's food supply stays secure and available.

Time-series forecasting is essential for system analysis. Many traditional studies have paid attention to individual stock-oriented solutions and disregarded general approaches on financial time series or skipped the dynamics of the system and its triggering components. It is difficult to fully adapt to evolving market conditions with stable financial indicators. For this reason, the proposed novel distance-based exponential moving-average (DBEMA) model is dynamically designed to overcome the changing conditions of financial time series. A novel distance-based moving-average feature model can produce an adaptive prediction approach for financial time series. To evaluate the impact of the novel proposed DBEMA features, they are compared to the features selected by recursive feature elimination using classification and regression trees among the financial indicators, using benchmark classification models. To confirm the performance of the proposed novel distance-based moving-average features, the forecasting results of the features are compared using linear regression, bagged trees regressor, Gaussian naive Bayes, k-nearest neighbors, random forests, multilayer perceptron, convolutional neural network, long short-term memory, gated recurrent unit, and relative strength index method benchmark models. The experimental analysis has shown that methods with our proposed novel DBEMA features has better forecasting accuracy with respect to the methods without DBEMA. Therefore, the proposed novel distance-based moving-average methodology designed for financial time-series analysis demonstrates that it guides a new perspective in nonlinear time-series trends.

This study investigates whether investors' overreactions to blockchain-related announcements is driven by corporate executive opportunism in the Chinese capital market. We manually collect blockchain-related announcements made by Chinese listed firms over 2016–2022 and conduct an event study to estimate market reaction. The announcements are further categorized as value-driven and speculation-driven announcements. Although both announcement types elicit positive short-term market reactions, the long-term reactions to speculation-driven announcements fade and even reverse, while the value-driven ones remain positive. Additionally, we determine that speculation-driven announcements are followed by seasoned equity offerings, negative news releases, and reductions in the stockholdings of large shareholders and executives, suggesting corporate executive opportunism. Finally, we demonstrate the efficacy of internal and external corporate governance in reducing corporate executive opportunism. Our study sheds light on market reactions to blockchain-related announcements from the perspective of executives’ opportunism.

This study explores whether financial literacy can enhance the ability to predict credit default by farmers using machine-learning models. It introduces a hybrid model combining k-means clustering and Adaboost to predict loan default using data on 10,396 farmers who obtained credit from Chinese rural commercial banks, including demographics, household finance, credit history, and financial literacy. We systemically compare the results of models with and without financial literacy variables, which indicate significant improvement in the predictive accuracy about credit risk when financial literacy factors are included. Our findings confirm that financial literacy is a crucial indicator of farmers' ability to make informed financial decisions, reducing their likelihood of loan default and suggesting its utility as a screening tool or supplementary credit risk assessment variable. This research has profound implications for financial inclusion and credit risk management, indicating that financial institutions can leverage financial literacy data to evaluate farmers’ creditworthiness and design effective financial education programs. This study enriches the literature on credit risk prediction by introducing financial literacy as a predictor of credit default.

The study addresses the pressing issue of environmental degradation, pinpointing carbon dioxide (CO₂) emissions as its primary driver, posing a threat to global environmental sustainability, including the member countries of the European Union (EU). Global warming problems persist, but previous studies have not adequately explored the factors that contribute to a reduction in carbon emissions in EU countries. The paper fills the gap by assessing the efficacy of carbon tax and eco-innovation in mitigating CO₂ levels from 1994 to 2019, considering the influence of renewable energy and various aspects of globalization. The study establishes long-term associations among the indicators examined using advanced methodologies, including the cross-sectional autoregressive distributed lag approach and the Westerlund cointegration method. Notably, the results highlight that carbon taxes, eco-innovation, renewable energy, and globalization contribute to slowing environmental deterioration, and economic progress plays a role in mitigating environmental sustainability challenges in EU member states. These findings reinforce the importance of robust strategies for reducing CO₂ emissions and minimizing adverse environmental impacts.

This study ranks 1235 companies operating in 10 different Islamic countries according to their degree of Shari'ah convergence and investigates the relationship between a firm's market-to-book ratio and convergence to Shari'ah rules. We determine Shari'ah screening variables by reviewing existing Shari'ah market indices and their screening rules; variable weights are determined using the criterion impact loss (CILOS) method, a well-known, multicriteria decision-making technique. The results show that the degree of Shari'ah convergence has a statistically significant negative effect on the market-to-book ratio, which can be seen as a proxy for the market value of equity.

This study evaluated the organizational resilience of Chinese listed companies under extreme heat events based on the cumulative abnormal return of stocks during severe environmental disturbances. Based on institutional, stakeholder, and risk management theories, the extreme heat event in August 2022 was chosen as the research setting to evaluate the relationships between environmental, social, and governance (ESG) performances and organizational resilience. Results showed that ESG performances significantly improve company resilience in market short-term response. The significantly improved resilience effects were observed in non-state-owned enterprises and companies in low-carbon pilot cities. Further analysis revealed that companies with varying levels of ESG performances demonstrate significant differences in long-term profitability potential. This study has important implications for further research on the effect of ESG performance on company resilience and provides a basis for enhancing company stability and flexibility during frequent extreme weather events.

By extracting detailed birth information for managers of Chinese listed firms from 2011 to 2021, we developed a novel measure of overconfidence and applied it to the corporate information disclosure. Our findings demonstrated a close association between managerial overconfidence and both mandatory and voluntary disclosure behaviors exhibited by firms. Specifically, managerial overconfidence significantly contributed to an increase in accrual-based earnings management and the voluntary disclosure of Corporate Social Responsibility (CSR) information by firms. Furthermore, we observed that overconfident managers were more inclined to adopt a positive disclosure tone and tend to underestimate future risks in forward-looking information disclosure. In our additional analysis, we uncovered evidence suggesting that various factors, including corporate governance practices, the types of actual controllers, financing constraints, analyst following, and market competition, might all play a role in influencing corporate information disclosure behavior driven by managerial overconfidence. This study expands on the economic implications of the Upper Echelons Theory and offers pertinent recommendations to enhance the quality of information disclosure.

This study investigates the association between infrastructure development in China's Belt and Road Initiative (BRI) and foreign direct investment (FDI) inflows to southern Africa, using a propensity score matching–difference-in-difference (PSM-DID) regression. The empirical examination is based on a sample of eight southern African countries selected based on data availability. We find that China's BRI has significantly positive effects on FDI inflows to southern Africa. Therefore, governments and development policy makers in southern African countries are recommended to devise supporting policies in order to ensure that the BRI leads to sustainable FDI inflows and sustainable economic growth. The study makes significant contributions to theory, policy, and practice. In policy and practice, our study is valuable for policy-making on driving FDI inflows. However, the context of the study is specifically southern Africa, hence, the results might not be generalizable to other African regions or Africa as a whole, so further research is needed to consider those areas.

This study employs the panel vector autoregression model to investigate how digital financial inclusion (DFI), technological advancement, environmental conditions, and economic growth are interconnected in 16 low financial development countries (LFDCs) and 29 high financial development countries (HFDCs) from 2015 to 2020. The results of the impulse response function reveal that in LFDCs, DFI enhances environmental quality while promoting technological progress. However, this improvement in environmental quality results in a decline in economic growth in these countries. In HFDCs, the promotion of DFI results in economic growth; however, it is accompanied by a decrease in environmental quality. Furthermore, the results of variance decomposition demonstrate that the interconnection among DFI, economic growth, environmental quality, and technological progress is more closely related in LFDCs than in HFDCs. Based on the findings, we recommend relevant policy implications for the respective country groups.