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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.

The study endeavors to explore whether the financialization of agricultural commodities, traditionally viewed as a catalyst for price volatility, has any repercussions on food security in BRICS economies.

The empirical analysis employs the examination of three agricultural commodities, namely wheat, maize and soybean. Utilizing data from the Chicago Board of Trade on futures trading for these commodities, we focus on parameters such as annual trading volume, annual open interest contracts and the ratio of annual trading volume to annual open interest contracts. The study spans the period 2000–2021, encompassing pre- and post-financial crisis analyses and specifically explores the BRICS countries namely the Brazil, Russia, India, China and South Africa. To scrutinize the connections between financialization indicators and food security measures, the analysis employs econometric techniques such as panel data regression analysis and a moderating effects model.

The results indicate that the financialization of agricultural products contributes to the heightened food price volatility and has adverse effects on food security in emerging economies. Furthermore, the study reveals that the impact of the financialization of agricultural commodities on food security was more pronounced in emerging nations after the global financial crisis of 2008 compared to the pre-crisis period.

This paper seeks to draw increased attention to the financialization of agricultural commodities by presenting empirical evidence of its potential impact on food security in BRICS economies. The findings serve as a valuable guide for policymakers, offering insights to help them safeguard the security and availability of the world’s food supply.

Very few studies have explored the effect of financialization of agricultural commodities on food security covering a sample of developing economies, with sample period from 2000 to 2021, especially at the individual agriculture commodity level. Understanding the evolving effects of financialization is further improved by comparing pre and post-financial crisis times.

The objective of the study is to analyse the impact of research and development investment on the firm performance of “small” scale firms vis-a-vis “medium”-scale firms.

The dataset comprised of a balanced panel of 486 research and development conducting Indian manufacturing small and medium enterprises, constructed for the period of 2006–2017. Fixed Effects, Random Effects Model and Hausmann test were used to analyse the determinants of firm performance in manufacturing small and medium enterprises in India.

It was found that from firms’ research and development (R&D) investments in terms of performance could be attained if simultaneously internationalisation and higher capital intensity could be achieved.

Managers could pay specific attention to the antecedents of firm performance and calibrate their R&D investment, internationalisation efforts and capital intensity simultaneously to achieve higher growth and productivity. For policymakers, the results provide an insight into how the firms in both categories could be differently incentivised, such that resources are better utilised.

The study analysed the determinants of firm performance in small and medium-sized firms at a disaggregate level as well as at a sectoral level using fixed effects, random effects and lagged effects to arrive at novel results, which have important implications for their competitiveness.

Market efficiency leads to transparent and fair price discovery of commodity markets, thus enhancing the value chain for competitive benefit. The purpose of this paper is to investigate the market efficiency of Indian agricultural commodities at spot, futures and mandi markets apart from exploring price risk management in these markets.

This study uses Johansen co-integration, vector error correction model and granger causality for analyzing market efficiency of the nine most liquid agricultural commodities across three markets, namely, spot, futures and mandi. All these nine commodities are traded on National Commodity and Derivatives Exchange.

The statistical results indicate price discovery exists in the mandi market and spot market leading to futures prices. Mandi price returns are seen to negatively influence futures returns in the case of cotton seed, guar seed and spot returns in the case of jeera, coriander and chana. For castor seed, the three markets are seen to have no long run relationship. The results of Granger causality reveal short run relationship between all the three markets in the case of soybean seed and coriander. In these commodities, prices in all three markets are capable of predicting the prices in the other markets. For the case of cottonseed, Rape Mustard seed, jeera, guar seed, the results indicate unidirectional causality between the mandi markets and the other two markets.

These results shall facilitate policymakers to explore intervention through integrated agri-platform (IAP) in price discovery and market efficiency.

The results of this study are useful in understanding the price discovery of mandi markets and its role in the spot and futures market. Agricultural commodities price discovery depends upon the integration of all these three markets. Introduction of IAP as described in the paper shall facilitate price risk management apart from improving the efficiency of price discovery.

To the best of the knowledge, this is the first study considering mandi, spot and futures prices in the price discovery process in India. In addition, this study found the role of mandi markets in serving the economic function of price discovery and price risk management. Hence, suggests for policy intervention for Indian agricultural commodities to manage price risk.

The study aims to analyze the impact of Research & Development (R&D) intensity on the firm’s performance, measured by growth of sales in the emerging market like India. Innovation strategy and its outcomes for firms may be different in developing countries as compared to developed countries. Thus, a study that focuses on the emerging economy like India, with a majority of the population dependent on agriculture, is of prime importance to the firm performance in the food and agricultural manufacturing industry. For this study, the broader focus will be on one widely recognised factor which may influence the growth rate of firms, i.e. investment in innovations which is in terms of R&D expenditure.

The paper investigates the relationship between the R&D efforts and growth of firms in the Indian food and agricultural manufacturing industry during 2001–2019. To empirically test the relationship between firm’s growth (FG) and R&D investments, system generalised method of moments technique has been used, hence enabling to avoid problems related to endogeneity and simultaneity.

The findings reveal that investments in innovations have a positive effect on the growth of firms in the Indian food and agricultural manufacturing industry. Investment in R&D also enables the firms to reap benefits from externalities present in the industry. Further analysis reveals that younger firms grow faster when they invest in R&D. More specifically, this paper finds evidence in the case of the food and agricultural industry that import of raw materials negatively affects the FG and export intensity positively affects the growth in the case of R&D firms.

This study suggests that the government should encourage the industries to invest optimally in R&D projects by providing favourable fiscal treatments and R&D subsidies which are observed to have positive effects in various developed countries.

To the best of the author’s knowledge, the current paper is the first to analyse the impact of innovation in food and agricultural industry on firm’s performance in an emerging economy context with the latest data. This paper agrees that a government initiative to increase private R&D expenditure would have favourable effects on FG as growing investments in R&D lead to further growth of the firms.

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.

Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance.

The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures.

Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm.

To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

This article attempts to understand the pattern of credit (loan) among agricultural households and identify the correlates of their access to institutional credit for policy imperatives. It also focuses on the inclusivity of institutional credit and debt pattern in terms of outstanding loan in the southern region of India.

This study employs the Tobit model along with the Heckman selection model to study the impact of various factors on the institutional borrowing and the amount outstanding.

The findings reveal that the access to credit is strongly associated with the socio-economic and demographic characteristics of agricultural households in South India. Asset position of households and size of holding are positively related with the probability of household having access to institutional credit. Education and family size are also found to be associated with higher access to formal credit. On the other hand, the socially disadvantaged households have lower access to formal credit. Similarly, other variables – assets, holding size and education – are associated with higher credit per household.

The findings indicate that the strategies to develop agriculture in southern India must encompass efforts to bring the small and marginal farmers under the coverage of institutional credit.

There are very few studies that have explored the credit access in South India from the perspective of land class despite the government’s attempts to include small and marginal farmers in the ambit of formal financial services.

Price discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets.

This study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX).

The VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India.

These results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market.

The findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery.

There are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

Innovation strategy and its outcomes may be different for agricultural input firms in developing countries than those operating in developed countries; hence, a study of developing economy should be an important addition to the literature which has earlier focussed mainly on developed countries. Indian firms which were previously catering to domestic demand are now the exporters of major agricultural machinery such as tractors and pesticides.

Rapid growth in demand for the agricultural inputs and improvement in technology implementations have led us to study the performance and transformation of these input industries. An empirical analysis was performed on the listed agricultural input firms during 2001–2019 to investigate the relationship between the R&D efforts and growth of firms in the seed, pesticide, fertiliser and agricultural machinery industries using the system-generalised methods of moments (GMM) technique on the panel of 1,320 firm-year observations.

The findings reveal that investments in innovations have a positive and lagged effect on the growth of firms in the Indian agricultural inputs industry. A further analysis reveals that younger firms grow faster when they invest in R&D. More specifically, the author finds evidence in the case of the agricultural inputs industry that import of raw materials negatively affects the firms' growth (FG) and export intensity (EI) positively affects the growth in the case of R&D firms. Investments in R&D are also seen to enable firms to reap benefits from externalities present in the industry.

This study suggests that the government should encourage the industries to invest optimally in R&D projects by providing favourable fiscal treatments and R&D subsidies which are observed to have positive effects in various developed countries.

There are very few studies that have explored the impact of R&D expenditure on the firm performance in agricultural inputs industry, especially in an emerging economy context like India.