This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.

Market Liquidity: Asset Pricing with Liquidity Risk

The study aims to identify the current trends, gaps, and research opportunities in research pertaining to the disruptive field of artificial intelligence (AI) applications in supply chain management (SCM). Since SCM represents managerial innovation due to its new way of integrated system thinking, SCM has emerged as one of the most fruitful business disciplines for AI applications. The study utilises bibliometric review in tracing the evolution of AI research in SCM and further synthesises decades of past AI research efforts to develop viable solutions for various supply chain problems and then proposes promising future research themes that would enrich supply chain decision-aid tools. The study identified five main research clusters through scholarly network and content analysis. The identified themes were: (a) supply chain network design (SCND), (b) supplier selection, (c) inventory planning, (d) demand planning, and (e) green supply chain management. As the role of AI in SCM continues to grow, there is a growing need for exploiting AI as a way to add value to supply chain process. The study proposes a research framework which will help academicians and practitioners in identifying current research patterns of AI in SCM.

The role of artificial intelligence in supply chain management: mapping the territory

We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt.

A DEA and random forest regression approach to studying bank efficiency and corporate governance

The potential and scale of cities enable economic growth and the improvement of citizens’ access to jobs, education, healthcare, culture, public utilities and services. Solid waste management (SWM) is one of the key services provided by cities. Its operations are complex, very visible to the general public, and impacted by strong financial, societal, and political constraints. Performances in developing countries, however, are not positive. United Nations research shows urban collection services covering no more than 39% of the population in low-income countries. Why are many cities in developing countries not able to use their increasing wealth and scale for basic SWM services such as city cleaning, collection, and sustainable landfilling? This paper provides a review of literature on this question over the last decade. It shows that research is mostly on symptoms and has not led to a deeper diagnosis of causes. Lack of resources, infrastructure, awareness, and institutional strength are often pinpointed as causes but, in fact, they should be addressed as symptoms. These symptoms should be designated as dependent variables in a complex causal network with systemic feedbacks, hindering or neutralizing attempts to improve performances if not properly dealt with. Research should concentrate more on assessing the relations between urbanization, urban processes, and urban governance that shape the performance of urban solid waste management. System dynamics modeling may provide new approaches for this diagnosis.

Failing Services on Urban Waste Management in Developing Countries: A Review on Symptoms, Diagnoses, and Interventions : A Review on Symptoms, Diagnoses, and Interventions

Understanding the potential applications of artificial intelligence in agriculture sector

The adoption of Artificial Intelligence (AI) in the food supply chains (FSC) can address unique challenges of food safety, quality and wastage by improving transparency and traceability. However, t...

Critical success factors influencing artificial intelligence adoption in food supply chains

Nonperforming assets (NPAs) crisis in Indian public sector banks is one of the biggest challenges before the current government in 2017. The finance ministry, Government of India, and the central bank, the Reserve Bank of India, are worried about the surging NPAs in Indian public sector banks because of their huge macroeconomic impact and systemic risk to the financial system. If not paid timely attention, it can hamper the economic and financial stability of the nation. Rising NPAs in Indian public sector banks are a result of bank specific, macroeconomic, and political factors. In order to control the surging NPAs in Indian public sector banks, the government and the Reserve Bank of India have implemented new crisis management framework which however is not immune to several challenges. This paper looks at NPAs crisis from the lens of crisis management, stakeholder's engagement, government relations, and issue management. Harris and Fleisher (2016) identify crisis management, stakeholder engagement, government relations, and issues management as important organizational activities that constitute part of public affairs. This paper follows a tripartite structure where it first investigates the causes of NPAs in Indian public sector banks. Second, it examines the crisis management framework developed by the policy makers and highlights the key challenges. Third, in light of these challenges, it makes recommendations to tackle the NPAs crisis in Indian public sector banks.

Lessons from NPAs crisis in Indian banks

Corporate governance challenges in Indian banks: A public affairs perspective

Banks as a channel of monetary policy transmission play a very important role in facilitating the process of credit creation in an economy. It is crucial for financial stability that banks stay financially healthy. Capital Adequacy Ratio (CAR) is an important indicator of banks financial health. By nature the banking-operations and performance are subject to business cycles and fluctuations in economic activity. The global financial crisis saw the failure of financial sector giants like Lehman Brothers and banks in the U.S.A and other countries. The spread of global financial crisis worldwide attracted the interest of academicians and policy makers in banking sector and its soundness. Thus, after the financial crisis of 2008 the research on banking sector particularly CAR has gained lot of attention worldwide. Our review of extant literature highlights scanty evidence of papers examining the influence of macro-economic indicators in influencing changes in CAR of banks. We believe macro-economic variables may have a significant impact on CAR of banks as financial soundness in banks also to a large extent depends on the external factors and the state of macro-economy. Hence, this paper attempts to investigate and evaluate the impact of macroeconomic indicators on CAR for Indian banks. The study uses dynamic panel data analysis on 65 Indian commercial banks from 2007 to 2013. The findings of the study based on Generalized Method of Moments reveals that Real GDP Growth, Inflation rate, Interest rate and Exchange rate have strong impact on CAR of Indian Banks. The findings of the study suggest that the policy prescriptions that focus on the bank-specific determinants of CAR should also appropriately account for the influence of macro-economic indicators. Policy makers and regulators like Reserve Bank of India should take into consideration the effect of macroeconomic variables on CAR while framing policies on CAR and monitoring banks.

Abhay Pant

Papers

Critical success factors influencing artificial intelligence adoption in food supply chains

A DEA and random forest regression approach to studying bank efficiency and corporate governance

Lessons from NPAs crisis in Indian banks

Corporate governance challenges in Indian banks: A public affairs perspective

Macro Determinants Of Car In Indian Banking Sector