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This paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money laundering (ML) and terrorism financing (FT) proceeds.

This paper provides a comprehensive review of ML/FT through the stock market across developed, developing and emerging jurisdictions, sheds light on the existing literature and critically evaluates the gap in the relevant studies. Moving forward, this paper develops the conceptual framework and formulates hypotheses to explore the empirical relationship.

This paper advocates and finds a basis to carry out much-needed empirical research between the ML/FT and stock market keeping in view the growing criminal cases in the developing countries. This paper suggests mining proxies from the publically available stock market data and the results of existing seminal research as variables of the study. These data and results carry information about the ML determinants. After developing hypothetical research providing concepts, this paper also finds that using a suitable methodology, preferable Bayesian logistic and linear regression models, it is possible to find the typologies and factors that can indicate and endorse the use of the stock market for ML/FT. Broadly, it is found that the significance of this study will be two-pronged: empirical development and policy implications.

This paper mainly focuses on the developing region, a newly emerging market and, peculiarly, a grey-listed region by the Financial Action Task Force (FATF).

In light of the existing literature and to the best of the researchers’ knowledge, this study will bring into focus the new age of the action research on the ML regime in the securities markets of the developing countries, hence, the emerging markets. Moreover, this research shall have a sheer significance for the policy measures on FATF recommendations on ML and FT, especially for the countries listed as “grey”.

The research based on comprehensive review will help in controlling the social behaviours aiding the proceeds of ML.

This research is extremely novel to the best of the researcher's knowledge.

This paper aims to identify, examine, and present an empirical research design of behavioral finance of potential investors during Covid-19.

A well-structured questionnaire was designed; a survey was conducted among potential investors using convenience sampling, and 200 valid responses were collected. The research work uses multiple regression and discriminant function analysis to evaluate the influence of cognitive factors on the financial decision-making of investors.

Recency and familiarity bias are proven to have the highest significant impact on the financial decisions of investors followed by confirmation bias. Overconfidence bias had a negligible effect on the decision-making process of the respondents and found insignificant.

Covid-19 is a temporary phase that may lead to changes in financial behavior and investors’ decisions in the near future.

The paper will help academicians, scholars, analysts, practitioners, policymakers and firms dealing with capital markets to execute their job responsibilities with respect to the cognitive bias in terms of taking financial decisions.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from literature on the chosen subject that no study has been undertaken to evaluate the impact of cognitive biases on financial behavior of investors during Covid-19.

This study aims to develop a theoretical model that uses the decision-making theory in a financial intermediation setting to provide insights into the differences between the outcomes of the decision-making process for a bank and for a peer-to-peer (p2p) lending platform to explain the role of p2p lending versus bank lending in the credit market.

This study develops a novel approach to explaining the differences between p2p lending and bank lending by using the decision-making theory. In particular, it analyzes the likelihood of a risky borrower being able to obtain a loan from a p2p lending platform versus the likelihood of being able to obtain a loan from a bank. The results contribute a theoretical understanding of factors that can determine the role of p2p lending platforms versus that of banks in the credit market, with implications for recovery from an economic crisis.

p2p lending platforms have the potential for contributing to economic recovery when they are subject to less regulations and are able to offer a faster and less costly lending process than do banks and when they are used by a large number of lenders. However, the potential role of p2p lending platforms in recovery might be reduced when banks have access to anticyclical measures that reduce banks’ capital requirements or provide them with low-cost funds.

This study provides a novel approach to explaining the differences between p2p lending and bank lending by using the decision-making theory. The results contribute a theoretical understanding of factors that can determine the role of p2p lending platforms versus that of banks in the credit market.

This paper aims to investigate the role of corporate governance on the bank profitability of Indian banks vis-à-vis South Asian Association for Regional Cooperation (SAARC) nations.

For the Corporate Governance Index, the authors examined board accountability, transparency and disclosure and audit committee, while Tobin’s Q, return on equity and return on assets are used to measure the bank’s profitability. The study used a two-stage analysis based on balanced panel data for robust findings. Sample of this study consists of 60 commercial banks from India and 60 banks from SAARC nations for the period of 2009–2021. This study used panel regression and a generalized method of moment approach using the CAMELS framework on banking industry-specific variables to determine their respective impacts.

The findings of this study suggest that board accountability is positive and significantly affects the profitability of banks as indicated by return on assets, return on equity and Tobin’s Q. In contrast, the audit committee has a positive and insignificant impact on return on assets, return on equity and Tobin’s Q, while transparency and disclosure have a negative and significant impact on these metrics. Furthermore, the country dummy result shows a significant positive impact on all the bank performance parameters, implying that Indian banks have the highest degree of convergence with corporate governance as compared to other SAARC nations.

This study provides insight to the regulators, policymakers and financial institutions to evaluate the role of corporate governance in emerging economies. However, the findings of the study should be interpreted with caution, as the results are sensitive to the disparity between India and other SAARC nations' government policies, climatic circumstances and cultural or religious traditions.

To the best of the authors’ knowledge, this is the first attempt to gauge the performance of Indian banks vis-à-vis SAARC nations using the CAMELS framework approach. Further, findings of this study suggest some novel evidence tying corporate governance quality with the profitability of banks among SAARC nations.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

The purpose of this study is to understand the behavior of hybrid bio-composites under varied applications.

Fabrication methods and material characterization of various hybrid bio-composites are analyzed by studying the tensile, impact, flexural and hardness of the same. The natural fiber is a manufactured group of assembly of big or short bundles of fiber to produce one or more layers of flat sheets. The natural fiber-reinforced composite materials offer a wide range of properties that are suitable for many engineering-related fields like aerospace, automotive areas. The main characteristics of natural fiber composites are durability, low cost, low weight, high specific strength and equally good mechanical properties.

The tensile properties like tensile strength and tensile modulus of flax/hemp/sisal/Coir/Palmyra fiber-reinforced composites are majorly dependent on the chemical treatment and catalyst usage with fiber. The flexural properties of flax/hemp/sisal/coir/Palmyra are greatly dependent on fiber orientation and fiber length. Impact properties of flax/hemp/sisal/coir/Palmyra are depended on the fiber content, composition and orientation of various fibers.

This study is a review of various research work done on the natural fiber bio-composites exhibiting the factors to be considered for specific load conditions.