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The purpose of this paper is to examine if the announcement of corporate power purchase agreements (PPAs) induce significant effects on the electricity buyers' stock returns.

This is an event study based on the Fama French Five Factor Model which uses several significance tests and robust regression approaches.

The announced closing of corporate PPAs induces significant positive abnormal stock returns. This announcement effect is even more pronounced in case of virtual PPAs.

To the best of the author‘s knowledge, this study is the first which explictly investigates the announcement effects of corporate PPAs, which are closed between the owner of the renewable energy asset and the institutional end consumer. In addition, this study extends the event study approach by robust regression methods.

As specific objectives, we intend to: (1) measure the connectivity between the spillovers of returns from the financial and nonfinancial sectors of the Brazilian stock market; (2) estimate the spillovers of individual returns for each sector to identify periods of higher and lower profits over a period of around eight years; (3) investigate the existence of relationships between these repercussions between pairs of sectoral indices, evaluating how much each specific sector transfers to each other and the market as a whole and (4) examine whether the connectivity of the Brazilian stock market itself and future interest rates in the USA and Brazil as well as the risk of the Brazilian economy, were explanatory variables of the dynamics of interdependence in the returns of these indices.

With a daily series of closing prices of sectoral indices from March 3, 2015, until June 21, 2023, we researched eight of the most relevant sectoral indices on the São Paulo Stock Exchange (B3). With this data, we estimate the Diebold–Yilmaz spillover index and frequency decompositions of Barunik–Krehlik.

The conclusions indicate that there is an overall connection of 66% in the financial and nonfinancial sectoral indices, with a peak of 83%. The consumer, energy and public services sectors stand out as significant sources of primary spillovers. When we classified secondary effects into periods, we saw that the shocks dissipated as time passed and the returns of the commodity index remained resilient across all periods.

Our conclusions highlight the influence of three main factors in sectors with a high degree of connectivity: periods of increased uncertainty; negative externalities in post-crisis periods and the impact of financial news on market sentiment. We think this study provides information that can be useful for policymakers, investors, investment portfolio managers, economists (financial, monetary and industrial), investment consultants and researchers who are interested in the complex interconnection among emerging market stock indices.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Understanding behavior toward sustainable consumption remains among the most challenging contemporary topics and requires continual investigation. The aim of this paper is to explore young Bahraini women’s level of awareness of sustainable consumption while also considering their attitudes, motivations and behavior as key elements in the study.

A qualitative approach was used to collect the data. More specifically, 46 semi-structured interviews with young Bahraini women took place and the results were produced using thematic analysis.

Results reveal that consumers’ awareness levels toward sustainable consumption have been growing but awareness remains variable among consumers. In turn, motivation toward supporting sustainability is more associated with environmental reasons than cultural or economic ones. The attitude toward sustainable consumption is generally positive among young consumers. Moreover, sustainable consumption has been found to be present through various means in the purchase decisions of young Bahraini women.

This study exclusively explores the awareness levels of young Bahraini women regarding sustainable consumption with a focus on the elements of the behavioral process, namely, “awareness, attitude, motivation, and behavior”, as central research pillars.

While an increasing number of investors value socially responsible investment practices, Bitcoin has faced criticism for its carbon footprint resulting from excessive mining power consumption. By examining Bitcoin’s interconnectedness with environmental, social and governance (ESG) equities, this study aims to construct a socially responsible investment strategy for cypto investors.

This study uses wavelet analysis and a time-varying parameter vector autoregressive (TVP-VAR) model to uncover the interdependence between ESG equities and Bitcoin. This study computes the optimal ratio, showing that Bitcoin significantly reduces portfolio risk when combined with green stocks.

The results show that co-movements between green stocks and Bitcoin are low, indicating that they are suitable combinations for portfolio diversification. From an environmental perspective, this investment strategy offers a theoretical solution to mitigate the negative impacts associated with Bitcoin mining. It aims to address the dilemma faced by sustainability-conscious investors, who must navigate the economic payoff of Bitcoin against their commitment to green investment principles.

The findings can provide valuable insights for policymakers seeking to develop strategies that promote sustainable investments among crypto investors.

Research on ethical investment practices in the cryptocurrency market remains in the early stages of development. Ethical investors can benefit from including Bitcoin in their ESG equity portfolios.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

The Indian pharmaceutical industry has contributed significantly to global healthcare by securing superior-quality, inexpensive and reachable medicines worldwide. However, supply chain management (SCM) has been challenging due to constantly shifting requirements for short lifecycles of products, the convergence of industry and changeable realities on the ground. This study aims to identify, assess and prioritize the strengths, weaknesses and opportunities of the pharmaceutical SCM environment in India.

The paper employs a Strength, Weakness, Opportunity, Threat (SWOT) analysis and recognizes strategies to utilize the advantages of the strengths and opportunities, rectify weaknesses and resolve threats.

A variety of strategies that could have a positive effect on the Indian pharmaceutical business are presented. Findings and suggested strategies can significantly advance knowledge, enhance understanding and contribute to the growth of a successful SCM for the Indian pharmaceutical sector.

This paper would act as a roadmap to greater comprehension of the market leaders and market leaders' operating climate. The findings from this study will offer academic scholars and business practitioners deeper insights into the environment of SCM.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

The purpose of the research is to investigate whether corporate social responsibility awareness (pCSR), environmental concerns (EC) and consumer environmental knowledge (CK) will have an impact on sustainable purchase intention (SPI). Furthermore, this paper also contributes to surveying the mediating impact of consumer attitudes (CAs) between intention and the three factors mentioned above.

SmartPLS (version 4.0) structural equation modeling (SEM) and quantitative methods were used to analyze 457 responses from consumers. The survey sample consisted of individuals between the ages of 18 and 34, with a male-to-female ratio of 70 to 30. The study aims to examine and put into practice new directions for manufacturing firms in the fields of fashion, food and consumer products. At the same time, provide more convincing evidence about the use of these fields in the research.

The study showed a favorable link between pCSR, EC, CK and SPI through the proposed hypotheses. The research additionally showed that CAs mediate between the aforementioned variables.

The important and distinctive results of this study encourage both consumers and enterprises to make changes in their perceptions of society. Consumers should gradually change their daily lifestyle by consuming more sustainable products. As a result, this outcome will provide the impetus for manufacturing businesses to alter their operational procedures in order to support the shift from the production of products to more friendly processes, with the help of all levels of management within the business.

This study aims to perform innovation analysis for a product based on market, design and process dimensions. This integrated approach provides sustainability for product design and development.

A significant aspect of innovation is investigated to provide energy from the wastes collected in the reverse chain. First, the indicators related to the product opportunity gap were collected and ranked by the structural equation modeling (SEM) method. Indicators with a factor loading above 0.6 are selected and inserted into the proposed mathematical model. The proposed mathematical model was implemented in GAMS 28.2.0 to maximize energy production from waste and minimize the cost of product innovation. A case study on pistachio new packaging process innovation is investigated.

The results showed that in today’s competitive world where sustainability and the environment are important, the index of converting waste into energy is one of the main indicators of innovation. Consequently, flammability is extracted from the mathematical model as one of the most significant indicators leading to higher energy production with the lowest innovation cost.

New product development (NPD) is significant to sustain market share and satisfy customer needs. Different approaches are proposed to handle NPD, mostly focusing on the customer and design requirements.