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This study aims to examine the impact of renewable energy consumption on agricultural productivity while accounting for the effect of financial inclusion and foreign direct investment in Brazil, Russia, India, China and South Africa (BRICS) countries during 2000–2020.

The study has used the latest data from World Bank and International Monetary Fund databases. The dependent variable in the study is agricultural productivity. Renewable energy consumption, carbon emissions, financial inclusion and foreign direct investment are independent variables. Autoregressive distributed lag (ARDL) approach was used to examine the short-run and long-run impact of renewable energy consumption, carbon emissions, foreign direct investment and financial inclusion on agricultural productivity.

The findings imply that consumption of renewable energy, carbon emissions and foreign direct investment have a positive impact on agricultural productivity while financial inclusion in terms of access does not seem to have any significant impact on agricultural productivity. Providing farmers, access to financial services can be beneficial, but its usage holds more importance in impacting rural outcomes. The problem lies in the fact that there is still a gap between access and usage of financial services.

Policymakers should encourage the increase in the usage of renewable energy and become less reliant on non-renewable energy sources which will eventually help in tackling the problems associated with climate change as well as enhance agricultural productivity.

Most of the earlier studies were based on tabular analysis without any empirical base to establish the causal relationship between determinants of agricultural productivity and renewable energy consumption. These studies were also limited to a few regions. The study is one of its kind in exploring the severity of various factors that determine agricultural productivity in the context of emerging economies like BRICS while accounting for the effect of financial inclusion and foreign direct investment.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.

This paper aims to investigate the relationship between institutional investors and sustainability disclosure, specifically examining the impact of financial performance as a moderator variable on the relationship between institutional investors and sustainability disclosure.

This paper used a panel data set of 51 firms from the industrial sector listed in the Amman Stock Exchange, with a total of 459 observations during 2013–2021. Multiple regression models were used to test the direct and moderating relationships.

The findings of this paper indicate that institutional investors exhibit varying attitudes toward sustainability disclosure. Institutional investors have a positive and significant impact on firm sustainability disclosure. Furthermore, the relationship between institutional investors and sustainability disclosure is significantly influenced by financial performance.

The study’s findings encourage regulators to enhance firms’ awareness of sustainability disclosures by balancing the economic, social and environmental pillars. This can be achieved by conserving natural resources, protecting the environment and promoting social justice for future generations.

The paper’s insights can be valuable for policymakers’ sustainable practices by encouraging institutional investors to support sustainability activities actively.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

This study explores the association between market efficiency and speculation. The government of India temporarily banned the futures trading of various commodities several times citing the presence of speculation. Many controversies exist about this topic; thus, this study clarifies the association between market efficiency and speculation and investigates whether market reforms altered this association.

The data for nine commodities is collected from the National Commodity and Derivative Exchange (NCDEX) for 2005–2022. Regression analysis and Automatic Variance Ratio (AVR) were adopted to inspect the informational efficiency and influence of speculation in the commodity market. Furthermore, this study uses different sub-samples to understand the changes in the market microstructure and its effects on market quality.

The results confirm an inverse and significant relationship between information efficiency and speculation and a deviation from the random walk process observed. Therefore, return predictability exists in the market. This study confirms that market reforms do not reduce the influence of speculation on market efficiency. The study concludes that the market is not weak-form efficient.

This study has certain limitations, since this study is empirical in nature, it may possess the limitations of empirical research.

This paper has dual novelty. First, this study investigates the effects of market reforms. Second, this study captures the influence of speculation in the Indian agricultural commodity market by considering the market microstructure aspects.

The main goal of this paper is to analyse the impact of political connections and gender diversity shaping Environmental, Social and Governance (ESG) components’ effects on the performance of Iberian companies.

To achieve this aim, we have used panel data methodology, specifically the generalized method of moments system estimation method by Arellano and Bond (1991), using data from listed Iberian companies for the period between 2015 and 2020.

Our findings suggest that, although ESG components positively influence company performance, the presence of political connections weakens ESG commitments, compromising ethical standards and suggesting a lack of transparency or inadequate regulations. Our results also highlight that the presence of women on boards of directors has a nuanced impact on firm performance, as measured by the Market-to-Book ratio. While gender diversity interacts with ESG scores, external investors' perceptions may not always reflect immediate performance improvements.

This work faces some limitations associated with challenges in securing comprehensive data for all variables, along with the complexity of acquiring information about political connections. Often, we had to rely on multiple sources and cross-reference the data to enhance its reliability. Another limitation for potential consideration or exploration in future research pertains to the omission of distinct industry sectors due to the limited number of companies, particularly notable in the context of Portugal.

Although there is a large volume of literature on the relationship between ESG and companies’ performance, as far as the authors are aware, this article is original and covers an important gap in the literature when considering political connections and board gender diversity impact on ESG components as determinants of the performance of Iberian companies.

From a target perspective, this paper aims to examine the impact of environmental, social and governance (ESG) performance on mergers and acquisitions (M&A) transaction valuations.

This paper uses a sample of 629 international transactions conducted between 2002 and 2020. Ordinary least squares (OLS) regression was applied by using ESG aggregate score and the three ESG pillars: environment, social and governance.

This paper finds that the ESG performance of targets has a negative and significant impact on acquisition premiums. However, this paper finds that targets receive lower premiums by increasing their ESG score, suggesting that targets would do better to focus on ESG to increase shareholder wealth. Thus, results of this paper support the view that ESG-focused firms create shareholder value through the M&A process. Furthermore, results of this paper indicate that environmental and social aspects of ESG drive the acquisition premium. The governance score does not seem to be related to acquisition premiums.

To the best of the authors’ knowledge, this study is the first study to assess whether ESG performance impacts the valuation of M&A transactions by decomposing ESG into its three components.

The aim of this study is to examine the ownership impact on firm sustainable investments (FSIs). In particular, this research examines the link between institutional investor ownership (IIO), managerial ownership (MOWN) and FSIs in the tourism industry in Malaysia.

This study uses a data set of 346 firm-year observations from 2008 to 2020 and applies feasible generalized least squares (FGLS) regression analysis. The study sample is based on tourism firms listed on Bursa Malaysia (the Malaysian Stock Exchange).

There is a significant positive association between IIO and FSIs. When IIO is classified into foreign (FIIO) and local (LIIO), this significant association is mainly driven by FIIO. In addition, there is a significant, positive association between managerial ownership (MOWN) and firm sustainable investments (FSIs). These findings imply that firm ownership has an influence on FSIs in the tourism industry.

This is the first attempt to consider IIO and MOWN simultaneously in a single model estimation. The findings contribute to emerging capital markets where the involvement of ownership concentration in the governance of publicly listed firms is a common practice.

With focus on Uganda, this study assessed the factors influencing agro-food micro, small and medium enterprises (MSME) innovations. Kampala, Wakiso, Mukono and Jinja districts were the locations of the research.

Primary cross-sectional data was collected using structured questionnaire for a sample of 521 agro-food MSMEs in Uganda. Descriptive statistics, exploratory factor analysis and hierarchical regression analysis were used to examine the data in SPSS.

The findings indicate that MSME innovation levels were usually high, at roughly 80%. The presence of rules that encourage innovation and reward creative people would enhance innovation that is customer-focused. On the other hand, policies and principles that encourage innovation and the conduct of internal product and process improvement research would promote system-focused innovation.

Encouraging agro-food MSMEs to develop policies that support innovation would improve the overall level of innovation, while building the capacity of agro-food MSMEs to conduct product and process improvement research would increase the level of systems-focused research.

This study assessed the drivers of innovation in agri-food MSMEs in a developing country. The uniqueness of this study is in assessing the effects of innovation support services on customer-focused and systems-focused innovations.

Limited studies in the mobile payment segment have attempted at understanding the factors that resist customers from using financial apps or mobile payment services (MPSs). This study aims at identifying the barriers from online customer reviews and examine how these barriers affect customers’ negative emotions (anger, fear, sadness), customer ratings and recommendation intentions.

This study, divided into three phases, has adopted a text-mining based mixed-method approach on 14,043 reviews present in Google PlayStore or App Store pages about financial apps used in India.

Phase 1 identified barriers like, “bad user experience”, “UPI failure”, “trust issues”, “transaction delays” from the reviews. Phase 2 found that “bad user experience” and “UPI failure” trigger both “anger” and “sadness”. “Transaction delays” and “money lost in transaction” stimulate “fear”. From the IRT stance, in Phase 3 this study has found that barriers like, “transaction error”, “UPI failure” (usage), “bad user experience” (image) and “trust issues” (tradition) have a significant negative impact on both customer ratings and recommendation intention.

The current study contributes to the existing literature on MPSs by identifying barriers from user generated content. Additionally, this study has also examined the impact of the barriers on customers’ negative emotions and recommendation intention.