With the rapid expansion of the blockchain ecosystem, crypto asset valuation has become an essential area of study for investors and institutions. Here, we introduce a deep learning framework that was designed to predict the value index of crypto assets by integrating intrinsic value variables and decomposing market prices into value and sentiment components. The Crypto Asset Value-indexing Model (CAVM) was applied to Ethereum's cryptocurrency ETH to demonstrate its effectiveness. Four econometric tests were conducted to verify the informativeness, predictiveness, and reasonability of the generated value indices, as well as the efficiency of price decomposition. Our findings suggested that the value index can serve as a reliable proxy for the intrinsic value of crypto assets, offering a benchmark for investment decisions, consumption, financial reporting, and potential tax implications. Additionally, this research contributes to the literature on asset valuation by proposing a novel method that applies deep learning techniques to intangible assets traded in secondary markets. By utilizing the end-to-end nature and directed acyclic graph structure inherent to deep learning models, we enhance the modeling process with customized loss functions and regularization mechanisms.

Using a panel smooth transition regression (PSTR) model, this study investigated the nonlinear impacts of climate risk on renewable energy development (RED) under different regimes of institutional environment, covering the panel data of 85 countries over the period of 2000–2022. The results show that climate risk negatively affects RED, and it exhibits nonlinear transformation characteristics under different regimes. Climate risk has differential impacts on RED in different types of institutional environments; however, the negative impacts of climate risk on RED can be mitigated in a stable economic and financial environment. In addition, the negative effects of climate risk are more pronounced in low-income countries than in high-income countries. Our findings have important implications for addressing the challenges of climate change and achieving sustainable development.

This study presents a numerical method for accurately computing the option values and Greeks of equity-linked securities (ELS) near early redemption dates. The Black–Scholes (BS) equation is solved using the finite difference method (FDM), and a Dirichlet boundary condition is applied at strike prices instead of directly replacing option values above the strike price with predefined option prices. This approach improves the accuracy of option pricing, particularly in the presence of early redemption structures. The proposed method is demonstrated to be effective in computing Greeks, which are crucial for risk management and hedging strategies in ELS markets. The computational tests validate the reliability of the method in capturing the sensitivities of ELS prices to various market factors.

This research examines the factors that influence the public's expectation for more information, acceptance or rejection of central bank digital currencies (CBDC). Using generative AI (ChatGPT 4.0), responses were simulated to mimic CBDC adoption scenarios, considering demographic attributes, such as gender, income, education, age, level of financial literacy, network effect, media influence, and merchant acceptance. A total of 663 synthetic responses were generated and analyzed using statistical methods and multinomial logistic regression to assess the probability of acceptance, rejection, or waiting for more information to decide. The chi-squared automatic interaction detection (CHAID) model showed a high performance in correctly classifying cases of acceptance, indecision, and rejection, presenting an accuracy of 92.6%. Multinomial logistic regression revealed that factors, such as educational level, financial experience, and income level, significantly influence the decision to accept a CBDC. This method also shows a high performance, as it obtained an accuracy of 96.4%. These results are in line with previous research and underline the effectiveness of generative AI as a reproducible and low-cost tool for analyzing hypothetical scenarios. Generative AI, with its algorithmic fidelity, has great potential for predicting human behavior in economic contexts. However, synthetic data may not capture the complexities and nuances of actual human decision making. As a result, certain contextual factors, emotional influences, and unique personal experiences that may significantly influence an individual's decision to accept or reject CBDC may be overlooked.

This paper used an event study to assess whether public information regarding the host of the Summer Olympic Games influenced the national host's stock market. We analysed all events for which the organiser was announced between the years 1978 and 2021, including events from the period 1984–2032. We confirm that the host announcement causes a statistically significant abnormal positive rate of return on the event day for future organisers. While this is true for blue-chip indices, we did not obtain statistically significant results for main market indices. On the markets of countries that did not obtain rights for the organisation of the analysed event, we found statistically significant abnormal negative (−0.8% on average) rates of return two days before the event and statistically significant abnormal positive (0.5% on average) rates of return one day before the event. Such results were obtained for both blue-chip indices and main indices.

Under the dual backdrop of accelerating global digital transformation and pursuing high-quality economic development in China, new digital infrastructure has increasingly emerged as a crucial link connecting technological innovation with social equity, playing a significant role in achieving inclusive growth. To explore its logical connections and transmission pathways, this study first constructed a comprehensive index of inclusive development from three dimensions—sustainable growth, poverty reduction, and opportunity equity—to measure inclusive development across Chinese provinces. It then employed panel regression and mediation effect models to examine the inclusive growth effects of new digital infrastructure and its transmission mechanisms. Finally, grouped regression and threshold models were used to test the heterogeneity and threshold characteristics of the inclusive growth effects of new digital infrastructure. The findings revealed that new digital infrastructure directly influences various dimensions of inclusive growth by promoting industrial development, reducing transaction costs, enhancing information accessibility, and fostering regional coordination. Simultaneously, it exerts indirect effects through human capital accumulation and industrial structure optimization. The study emphasizes that these promotional effects vary depending on resource endowments and development stages, while also being constrained by threshold conditions such as transportation infrastructure and the degree of marketization. This indicates that new digital infrastructure has become a crucial strategic support for mitigating uneven and inadequate regional development, contributing to regional coordination and sustainable growth.

This paper contributes to the literature on capital structure by analyzing the determinants of the adoption of zero leverage by Portuguese SMEs. The aim is to assess whether financial constraints, financial flexibility, profitability, and an external shock (COVID-19) affect the likelihood of a company operating without debt. Based on a panel of data for the period 2018–2023, a dynamic probit regression model was estimated to analyze the persistence of zero debt and its main determinants. The results indicate that smaller and older companies are more likely to adopt a zero-debt policy, partially confirming the financial constraints hypothesis. Also, liquidity, tangibility, profitability, and the pandemic context do not significantly influence the adoption of that policy, indicating that other factors, such as the financial system's structure and credit barriers, may play a prominent role. One of the main results is the high persistence of zero leverage over time, suggesting that this decision is the result of a deliberate strategy. The findings of this study provide valuable insights into the financing decisions of Portuguese SMEs and present implications for managers, investors, financial institutions, and policymakers.