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The financial system of an economy, especially banking, facilitates efficient allocation of resources from savers to borrowers for productive investments, and thus promotes economic growth. State-wise bank credit in India shows a growing divergence, despite the aim of central planning to reach a degree of convergence in macroeconomic performance over time. This chapter analyzes how diverging bank credit affects macroeconomic performances of the Indian states, through an alternative approach of composite indicators-based rankings of states adopting the methodology of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) that is used in operations research or more specifically MCDM (multiple criteria decision-making). A composite indicator of the states’ annual macroeconomic performances has been constructed taking indicators of output growth, per capita state domestic product, inflation, and fiscal indicators for years 2006–2018. States are ranked by both macroeconomic performance and bank credit to states, and the correlation between the two indicators, known in the literature to be interlinked,is studied here to understand how the availability of credit or lack of it has influenced State level macroeconomic development in India. The results thus show that wealthier and better performing states continue to attract the larger chunk of bank credit, while weaker states have not been able to catch up. An important policy implication would be to place even more emphasis on higher levels of credit growth for weaker states, particularly infrastructure credit, to achieve a degree of income convergence throughout the Indian economy.

The global organic market is expanding, and India is in an advantageous position with the highest number of organic producers worldwide. Although many articles have been published on the value chain of organic products from India, no significant studies were found related to the value chain analysis of organic pineapple. This study aims to know the various aspects of the organic pineapple value chain, i.e. network structure, value addition at various stages of chain actors, value chain upgradation and governance structure.

The study is explorative in nature, and primary data from various actors involved in the chain is collected and analyzed. Primary data through a structured schedule and interviews are collected from farmers and traders. A multistage sampling plan has been adopted. A sample of 75 farmers was randomly selected from the study area. For traders, snowball sampling is used due to the nonavailability of the sampling frame. A total of 10 commission agents, 10 wholesalers and 20 retailers were thus selected for the study. For objectives 1 and 4, descriptive statistics are used. For objective 2, a modified formula described by (Murthy et al., 2007) is used to calculate farmer’s net price and marketing margin. For objective 3, Garrett’s ranking technique is used to identify various constraints in upgrading the organic pineapple value chain in Assam.

This study shows that the value chain of organic pineapple is in the initial stage and proper value addition is required to have a complete regulated value chain. Six marketing channel is identified, and products are sold through farmer producer company only in case of export and trade with distant buyers. The marketing efficiency for channels II and III is 1.69 and 0.99, respectively. The degree of value addition for channel II in the hands of the commission agent, wholesaler and retailer is 11.65%, 4.56% and 12.60%, respectively. In the various constraints in upgrading the value chain, farmers rank “policy support” as a major constraint. In the governance structure, trade with distant traders and exports is done formally and through written contracts.

The study performs value chain analysis of organic pineapple in Cachar district of Assam, India for the year January 2022–January 2023. Future studies are encouraged related to various aspects of the supply chain and value chain of organic pineapple from various northeastern states of India and other states.

The study will help policymakers and key actors to know the existing chain and frame a well-coordinated and regulated value chain.

This study is one of the first study to explore the value chain of organic pineapple of Cachar district of Assam, India. Implementation of these findings can help various actors to strengthen the existing value chain.

As the supply chain is a highly integrated infrastructure in modern business, the risks in supply chain are also becoming highly contagious among the target company. This motivates researchers to continuously add new features to the datasets for the credit risk prediction (CRP). However, adding new features can easily lead to missing of the data.

Based on the gaps summarized from the literature in CRP, this study first introduces the approaches to the building of datasets and the framing of the algorithmic models. Then, this study tests the interpolation effects of the algorithmic model in three artificial datasets with different missing rates and compares its predictability before and after the interpolation in a real dataset with the missing data in irregular time-series.

The algorithmic model of the time-decayed long short-term memory (TD-LSTM) proposed in this study can monitor the missing data in irregular time-series by capturing more and better time-series information, and interpolating the missing data efficiently. Moreover, the algorithmic model of Deep Neural Network can be used in the CRP for the datasets with the missing data in irregular time-series after the interpolation by the TD-LSTM.

This study fully validates the TD-LSTM interpolation effects and demonstrates that the predictability of the dataset after interpolation is improved. Accurate and timely CRP can undoubtedly assist a target company in avoiding losses. Identifying credit risks and taking preventive measures ahead of time, especially in the case of public emergencies, can help the company minimize losses.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

The academic literature has examined why bank runs happen based on the work of 2022 Nobel Prize-winning economists Diamond and Dybvig. They have found the source of banking/financial crisis in terms of mismatch between liabilities (deposits being short term and savers wanting to short-term access to their money) and assets (long term and illiquid). The Lakshmi Vilas Bank (LVB) crisis intensified when it came under Prompt Corrective Action (PCA) of the Reserve Bank of India (RBI). This situation provides the opportunity to study whether the elements embodied in the theoretical models like Diamond and Dybvig hold true for LVB crisis. This study aims to examine the reasons for the demise of LVB in India using DuPont financial model, peer group analysis and time series structural break in crucial financial parameters.

The study examines the reason for insolvency of LVB using financial ratios, financial models (DuPont), financial distress model (Z-score) and asset-liability management. The study also adopts univariate structural break models using quarterly financial data covering the key financial measures used in the RBI’s PCA framework.

LVB crisis is like Diamond–Dybvig model, in the sense, savers requiring short-term access to their money (liquidity for their deposits) on the information of high non-performing assets, which further deteriorates the illiquid nature of loan portfolio (assets) of banks. The study finds its profit margin (net interest margin and non-interest margin) and managerial efficiency had started deteriorating since 2018. The study finds that LVB’s main weakness lies in its limited credit appraisal ability, its monitoring and weak internal controls. Lending to sensitive sectors (like real estate, capital markets and commodities) and exposure to large business groups also contributed to its weakness. The study also finds huge, elevated asset-liability mismatch, especially in the short-term maturity buckets. Using univariate econometric time series model, the study also confirms financial weakness being evident much earlier than the time when resolution was undertaken by the RBI through PCA.

The study has implications for analysing and monitoring financial distress of banks. The study also has implications for devising banking regulation and supervision.

The study brings in a perspective of the banking regulations using the application of PCA framework on a listed private sector bank. The authors combine an accounting ratio model and combine risk measures that could identify the incipient risks in a bank. The authors believe this will help in refinement of banking regulations and better monitoring mechanisms.

The current study aims to examine the impact of two black swan events on the performance of six stock markets in Gulf Cooperation Council (GCC) economies (Abu Dhabi, Bahrain, Dubai, Oman, Qatar and Saudi Arabia). The two selected black swan events are the US Mortgage and credit crisis (Global Financial Crisis of 2008) and the COVID-19 pandemic.

The performance of all the six stock markets are represented by their return and price volatility behavior, which has been measured by applying ARCH/GARCH model. The comparative analysis is done by employing mean difference models. The data is collected from Bloomberg on a daily frequency.

The response of two black swan events on the GCC stock markets has been heterogenous in nature. During the financial crisis, the impact was heavily felt on most of the stock markets in the GCC countries. It is revealed that the financial crisis had a negative significant impact on four of the six countries. Whereas during the COVID-19 crisis, it is revealed that there is no significant impact on four of the six selected stock markets. The positive significant impact is felt on two stock markets, namely, the Abu Dhabi stock market and the Saudi stock market.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from the literature on the chosen subject that no study has been undertaken to evaluate and contrast the impact of the GFC crisis and COVID-19 on the GCC stock markets.

Motivated by the negative effect of external shocks on the domestic economy, this study explores the role of financial sector development in absorbing the effect of external shocks on macroeconomic volatility in Nigeria.

Autoregressive distributed lag and fully modify ordinary least square are used to examine the moderating effect of financial development in the link between external shocks and macroeconomic volatilities in Nigeria between 1986Q1 and 2019Q4. External shock is proxy using oil price shock, and financial development is proxy by domestic credit to the private sector and market capitalisation. At the same time, macroeconomic volatility is proxy by output and inflation volatilities. Macroeconomic volatilities are generated using generalised autoregressive conditional heteroskedasticity (GARCH 1,1).

The results indicate that domestic credit to the private sector significantly reduces output and inflation volatilities in Nigeria in the short and long run. However, market capitalisation promotes macroeconomic volatility. More specifically, financial development indicators play different roles in curtaining macroeconomic volatilities. The results also reveal that external shocks stimulate macroeconomic volatility in Nigeria in the short and long run. Nevertheless, the effects of external shocks on macroeconomic volatilities are reduced when the role of financial development is incorporated.

This study, therefore, concludes that strong financial sector development serves as a significant shock absorber in reducing the adverse effect of external shock on the domestic economy.

This study contributes to the extant studies by introducing a country-specific analysis into the empirical examination of how financial development can moderate the influence of external shock on macroeconomic volatilities.

This paper aims to examine landlords’ health support systems to tenants to control COVID-19 in selected informal settlement rental housing (ISRH) in Ghana, dwelling on landlords’ views.

The paper used the concurrent imbedded mixed-methods approach and grounded the findings in the socio-ecological theory. The authors collected both qualitative and quantitative data from 242 landlords in 13 informal settlements across Ghana using quotas. The authors undertook semi-structured face-to-face and telephone interviews. The authors conducted content and thematic qualitative data analysis and used simple descriptive statistical data analysis.

The paper discovered that tenants had limited knowledge on the transmission of the pandemic, forcing landlords to regulate their building services usage, ventilation and thermal control, entertainment, common areas and rent advancement for tenants to control the pandemic. Also, tenants found it difficult to comply with the rules on ventilation for fear of criminal attacks, while high social connection and interaction among renters and inadequate enforcement caused the non-adherence by renters to social gathering. Again, landlords had difficulty in contract-tracing visitors suspected to be infected with the virus.

The use of concurrent and imbedded mixed methods to investigate landlords’ viewpoints on their support in health needs of their tenants to regulate COVID-19. The prescriptions from the study provide practical applications to formulate a mix of housing and health policies to formalize the support of landlords to their tenants in ISRH in Ghana.

This paper provides a quantitative assessment of the “asset ratio” rule defined in Turkey as part of measures taken to stimulate the economy amid the Covid-19 pandemic. The main objective of the new rule was to boost credit growth in the economy and provide lending for credit-constrained households and firms that are in need. A secondary aim was to shift the denomination structure of the deposits toward domestic currency. Hence, the paper focus particularly on how the policy affected the growth rate of loans and the share of domestic deposits relative to foreign ones among the commercial banks. The policy was also heavily criticized due to the possibility that it will subjugate the banking system to excessive risk. The paper explore this possible impact by measuring how much the policy affected the default risk allowances in the banking system.

The new policy required banks with deposits above a threshold level, i.e. large banks, to maintain a certain asset ratio. Banks with deposits below the threshold, i.e. small banks, were held exempt from it. The paper implement a difference-in difference methodology to assess the quantitative impacts of the asset ratio policy by taking large banks as the treatment group, and small banks as the control group.

Difference-in-difference estimation results suggest that the asset ratio policy resulted in a 9.6% rise in loans and an 8.4% rise in government securities. Deposits also increased, with no significant change in their composition. The policy initially generated a 7% increase in the credit risk allowances of banks in the treatment group, which vanished in the following periods. Based on all these, the paper argue that the policy was successful in providing liquidity to the economy without jeopardizing the financial stability.

The findings of this study show that asset ratio policy is effective in increasing credit growth in countries with limited policy space such as Turkey. While saying this, the importance of the robust and prudent structure of the banking system in the economy should be underlined. Otherwise, the policy may have an unintended consequence of raising systemic risk. The policy suggestions also apply to advanced countries where the monetary policy has reached a natural limit due to the zero lower bound (ZLB). The ZLB problem encouraged these countries to use quantitative easing schemes in the aftermath of the Covid-19 crisis, just like the global financial crisis. However, it may take a long time to undo the effects of this policy on the balance sheets of central banks. In such cases, asset ratio policy can also be considered as an alternative tool for advanced economies notwithstanding the fact that the banking system should be prudent, well-capitalized and the country should have enough fiscal space. The main objective of the asset ratio policy was to help SMEs that were in urgent need of liquidity at the beginning of the crisis. The bank balance sheet data used in this paper does not contain information about the borrowers of the loans extended during the implementation of the policy. Analysis of this dimension using matched bank-firm level data will better demonstrate the success of the policy in achieving this goal. The paper address this as the main limitation of the paper and leave that analysis for future research.

This paper provides an important contribution to the literature by assessing a new unique policy whose objective is to stimulate loans and mitigate the impact of the Covid-19 crisis on the economy. The policy in question is predicted to have effects on the asset and liability structure and risk exposure of the banking system in Turkey. The quantitative analysis in this study estimates these impacts and discusses the effectiveness of the new policy in providing a relief for firms and households in need. Whether or not the policy caused a disruption in the sound structure of the banking system in Turkey is another question addressed in the paper.

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.