Search results

The COVID-19 pandemic has exposed the financial-economic vulnerability of the public and threatened the household financial stability, especially of the low-income group population, in developing economies such as India. The assessment of household financial vulnerability has gained considerable attention these days, especially in poor and developing countries. This article seeks to assess the level of household financial vulnerability in India, based on a household survey conducted across India.

This paper has proposed a financial vulnerability index (FVI) based on three self-reported parameters: (1) making end meet, (2) perception of income shock and (3) perception of expenditure shock. Subsequently, the impact of various behavioural and socioeconomic factors on the proposed financial vulnerability index has been assessed using fractional probit regression.

The research findings indicate that higher financial knowledge, better money management skills and lower impulsivity in financial behaviour can reduce financial vulnerability. It is suggested that suitable financial literacy programmes be implemented for vulnerable sections of society to enhance their financial knowledge, improve money management skills and manage impulsivity, thereby helping them make informed financial decisions leading to their financial well-being.

To the best of the authors’ knowledge, none of the past studies have developed and assessed the financial vulnerability index in India. This study provides relevant recommendations for various financial sector regulators and government institutions in India.

Over the period, the role of finance has emerged significant in the socio-economic development of the women. There are two major types of finances, i.e. formal and informal ones. Thus, the purpose of this paper is to investigate first the determinants of the demand for credit and then the demand for these credit sources by women especially in urban slums.

In this study, a primary survey was conducted with the help of a structured questionnaire in slums of two major urban cities in India, i.e. Delhi and Mumbai. In total, 450 individuals were interviewed in each city.

This paper presents a range of significant socio-economic factors affecting the demand for credit and source of credit by women borrower in Delhi and Mumbai. Despite, the greater emphasis by the government to increase the formal credit utilization, the informal credit is still preferred.

The outcomes of the study are expectedly useful to various policymakers and banks in encouraging women to opt more for the formal credit. The government can follow the research outcomes to scale up the programmes and schemes targeted for women empowerment in urban slums.

The study is unique of its kind in doing a comparative analysis in slums of two differently located urban cities with large slum population.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for the chickpea legume in major chickpea markets in India.

In this paper, the authors employ the Johansen co-integration test, Granger causality test, vector autoregression (VAR), and vector error correction model (VECM) to examine the integration of markets. The authors use monthly wholesale and retail price data of the chickpea crop from select markets in India spanning January 2003–December 2020.

The results of this study strongly confirm the co-integration and interdependency of the selected chickpea markets in India. However, the speed of adjustment of prices in the wholesale market is weakest in Bikaner, followed by Daryapur and Narsinghpur; it is relatively moderate in Gulbarga. In contrast, the speed of adjustment is negative for Bhopal and Delhi, weak for Nasik, and moderate for retail market prices in Bangalore. The results of the causality test show that the Narsinghpur, Daryapur, and Gulbarga markets are the most influential, with bidirectional relations in the case of wholesale market prices. Meanwhile, the Bangalore market is the most connected and effective retail market among the selected retail markets. It has bidirectional price transmission with two other markets, i.e. Bhopal and Nasik.

This paper calls for forthcoming studies to investigate the impact of external and internal factors, such as market infrastructure; government policy regarding self-reliant production; product physical characteristics; and rate of utilization indicating market integration. They should also focus on strengthening information technology for the regular flow of market information to help farmers increase their incomes.

Very few studies have explored market efficiency and direction of causality using both linear and nonlinear techniques for wholesale and retail prices of chickpea in India.

This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process.

A longitudinal single-site ethnographic case study using digital processing to make a material receiving process Lean was adopted. An inherent knowledge process with internal stakeholders in a stimulated situation alongside process requirements was performed to achieve quality data collection. The results of the narrative analysis and process observation, combined with a literature review identified widely used Lean tools, wastes and biases that produced a model for the relationships.

The study established the relationships between bias, Lean tools and wastes which enabled 97.6% error reduction, improved on-time accounting and eliminated three working hours per day. These savings resulted in seven employees being redeployed to new areas with delivery time for products reduced by seven days.

The single site case study with a supporting literature survey underpinning the model would benefit from testing the model in application to different industries and locations.

Application of the model can identify potential relationships between a group of human biases, 25 Lean tools and 10 types of wastes in Lean manufacturing processes that support decision makers and line managers in productivity improvement. The model can be used to identify potential relationships between forms of human biases, Lean tools and types of wastes in Lean manufacturing processes and take suitable remedial actions. The influence of biases and the model could be used as a basis to counter implementation barriers and reduce system-wide wastes.

To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes with waste production and human biases. As part of the process, a relationship model is derived.