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The purpose of this paper is to propose a model that will demonstrate how the integration of Salam (exclusive agricultural commodity trade) with Takaful (micro-Takaful – a subdivision of Islamic insurance) and value chain can address major challenges facing the agricultural sector in Kano State, Nigeria.

The study conducted a thorough and critical analysis of relevant literature and existing models of financing agriculture in Nigeria to come up with the proposed model.

The findings indicate that measures undertaken to address the major challenges fail. In view of this, this study proposed Bay-Salam with Takaful and value chain model to solve a number of challenges such as poor access to financing, poor marketing and pricing, delay, collateral requirement and risk issues in order to avail farmers with easy access to finance and provide effective security to financial institutions.

The paper is limited to using secondary data. Therefore, empirical investigation can be carried out to strengthen the validation of the model.

The study outcome seeks to improve the productivity of the farmers through enhancing their access to finance. This will increase their level of production and provide more employment opportunities. In addition, it will boost financial inclusion, income generation, poverty alleviation, standard of living, food security and overall economic growth and development.

The novelty of this study lies in the integration of classical Bay-Salam with Takaful and value chain and create a unique model structure which the researchers do not come across in any research that presented it in Nigeria.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

This study aims to examine the impact of the beginning of the Russia–Ukraine war and the Wagner Group’s attempted military coup against Putin’s regime on the European defense sector, consisting of weapons manufacturers.

The authors use the event study methodology to quantify the impact. That is, the authors assume that markets are efficient, and abnormal stock returns around the event dates capture the magnitudes of the impacts of the two events studied on European defense sector companies. The authors use the capital asset pricing model and two different multifactor models to estimate expected stock returns, which serve as the benchmark necessary to obtain abnormal returns.

The start of the war on February 24, 2022, when the Russian forces invaded Ukraine, was followed by high positive abnormal returns of up to 12% in the next few days. The results are particularly strong if multiple factors are used to control for the risk of the defense stocks. Conversely, the authors find a negative impact of the rebellion initiated by the mercenary Wagner Group’s chief, Yevgeny Prigozhin, on June 23, 2023, on the abnormal returns of defense industry stocks on the first trading day after the event.

To the best of the authors’ knowledge, this is the first study of the impact of the Russia–Ukraine war on the defense sector. Furthermore, this is the first study to measure the financial implications of the military coup initiated by the Wagner Group. The findings contribute to a rapidly growing literature on the financial implications of military conflicts around the world.

This paper reviews the recent collapse of two cryptocurrency enterprises, FTX and Celsius. These two cases of institutional bankruptcy have generated criminal charges and other civil complaints, mainly alleging fraud against the CEOs of the companies. This paper aims to analyse the fraud leading to these bankruptcies, drawing on key concepts from the research literature on economic crime to provide explanations for what happened.

This paper uses a case study approach to the question of how large financial institutions can go off the rails. Two theoretical perspectives are applied to the cases of the FTX and Celsius collapses. These are the “normalisation of deviance” theory and the “cult of personality”.

In these two case studies, there is an interaction between the “normalisation of deviance” on the institutional level and the “cult of personality” at the level of individual leadership. The CEOs of the two companies promoted themselves as eccentric but successful examples of the visionary tech finance genius. This fostered the normalisation of deviance within their organisations. Employees, investors and regulators allowed criminal and highly financially risky practices to become normalised as they were caught up in the attractive story of the trailblazing entrepreneur making millions in the new cryptoeconomy.

This paper makes a contribution both to the case study literature on economic crime and to the development of general theory in economic criminology.

Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

Micro-enterprises, like any other business entity, face financing challenges. However, micro-enterprises often cannot access financial institutions as they cannot fulfill the conditions to obtain financing facilities from a formal financial institution. As such, they have to rely on family or friends for financing needs. The most critical challenge faced in this regard to Muslim micro-enterprises is finding out a way in which family and friends could give a financial helping hand without the involvement of riba (interest). At the same time, the person giving the financing can enjoy a profit. This paper aims to propose the Tawarruq Fardi Financing (TFF) model that Islamic micro-enterprises could use to fulfill their financial needs. It becomes a solution when obtaining financing from friends and family or any other third party who could be a private investor who does not want to engage in an equity relationship.

This study adopts a qualitative research methodology, combining descriptive and content analysis using the inductive reasoning approach.

The paper's outcome shows that the proposed TFF could assist Islamic micro-enterprises in obtaining Shariah-compliant financing without engaging in an equity partnership. It allows them to fulfill their financing needs bearing in mind the interest of both parties involved in the transaction.

This research will assist Islamic micro-enterprises to find out a Shariah-compliant financing facility from family, friends and any other private investors without entering into an equity relationship. The proposed model would be a Shariah-compliant alternative to interest-free loans and engaging in an equity relationship for the sake of getting benefits for both parties involved in the transaction.

Organisations are confronted with the challenge of navigating various pressures arising from activities that shape environmental and social impacts, which stakeholders find significant. This research endeavours to ascertain a process facilitating the analysis and seamless integration of sustainability into corporate strategy. The goal is to establish an “integrated” ESG governance framework adept at effectively managing institutional pressures.

This research employs an action research approach, focusing on a leading company within the sugar industry. The investigation delves into the relationship dynamics associated with business issues through a process that engages, either directly or indirectly, board members, top managers, as well as industrial and commercial customers, along with final consumers.

The formulation of a sustainability strategy serves as a guiding framework for the Board of Directors in effectively navigating tensions arising from environmental, social and economic pressures.

The research contributes to bridging the realms of business governance and institutional theory (viewed under a paradoxical lens). On a managerial level, the study introduces a structured process aimed at seamlessly integrating sustainability objectives into governance, aligning with international ESG guidelines (OECD, 2023; WEF, 2020).

The originality of this research lies in crafting a sustainability strategy by the BoD that takes into account the impact of governance and responds to the demands of strategic stakeholders.

While gastronomy has become a major tourist attraction for rural territories, its study in protected areas remains underexplored. These places use their cultural and natural heritage and landscape, as well as their local identity, to charm visitors. Gastronomy plays a key role in this, and restaurants do their best to promote local food products. The aim of this article is to use a qualitative and case study approach to analyze the marketing and touristic impact of Prades potatoes (“Patata de Prades”), which are certified with a Protected Geographical Indication.

A total of 16 semi-structured interviews with restaurateurs (11) and producers (4) and the manager of the local cooperative (1) are the basis for examining the importance of this potato as a form of tourist attraction, its commercialization processes and the role it plays in the development of this protected rural region of Catalonia, in the north-east of Spain.

The results show that despite the great success of restaurants and food-based tourism with regard to Prades potatoes, the sustainability and future of this starchy vegetable are not guaranteed due to the scarcity of production, as it is a seasonal commodity and there is a lack of generational replacement in farming.

Food tourism that relies on local produce represents a “green” model for community development, but a specific public program is required to support the agricultural sector in the isolated areas to prevent the disappearance of this economic sector.