Template-Type: ReDIF-Article 1.0 Author-Name: Jared Dominic Fernandez Author-X-Name-First: Jared Dominic Author-X-Name-Last: Fernandez Author-Name: Arya Kumar Author-X-Name-First: Arya Author-X-Name-Last: Kumar Title: A machine learning perspective of the impact of COVID-19 on the Indian stock market Abstract: Stock markets across the globe were affected by the outburst of COVID-19 in early 2020. This attracted researchers to analyse and understand the implications of such sudden happenings on stock prices, more so by application of latest methodologies that are slowly finding greater relevance in social sciences. This study uses econometric and machine learning techniques to measure the impact of the COVID-19 pandemic and predict the future trend of the stock market in India. This paper attempts to examine the reliability of traditional methods and machine learning techniques to establish their relevance in predicting stock market trends. The study also uses variable perturbation and least absolute shrinkage and selection operator (LASSO) to identify which variables have more significant predictive weightage in the machine learning models. The study reveals that machine learning models outperform econometric models in their predictive power amidst more significant uncertainty. Moreover, a gated recurrent unit (GRU) model is able to capture the stock market dip and gradual recovery much better than a long short-term memory (LSTM) model. The findings of the study reveal that the number of cases and deaths had a significant impact on stock prices and predictive ability to forecast the NIFTY close price. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 1-22 Issue: 1 Volume: 2 Year: 2024 Keywords: stock market prediction; machine learning; long short-term memory; LSTM; gated recurrent unit; GRU; variable perturbation; COVID-19; NIFTY; Indian stock market. File-URL: http://www.inderscience.com/link.php?id=139145 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:2:y:2024:i:1:p:1-22 Template-Type: ReDIF-Article 1.0 Author-Name: Omer Hag Hamid Author-X-Name-First: Omer Hag Author-X-Name-Last: Hamid Title: E-Salam finance for risk management Islamic fin-tech mobile-based micro-finance approach Abstract: The objective of this paper is to examine how disruptive technological innovations can mitigate the risk of microfinance operations using forward sales and to explore the extent to which this approach is tailored to the financial needs of farmers in rural areas. Quantitative methods used. Various sources were used to collect secondary data. A questionnaire was used to manage the primary data through MFIS staff to examine the research hypothesis. Questions coded and used in Smart PLS for analysis. Firstly, the paper proposed a tool to reduce risk through the risk-sharing mechanism and structure of Salam finance. Secondly, the report suggested a mode of microfinance to increase income, as MFIs have significant competitive advantages over fin-tech. Third, the paper gives MFIs the chance to offer positive social impacts through financial services. This paper offers a mobile application approach for microfinance. Can used to satisfy small business owners in agricultural activities; the first paper provides the e-Salam finance structure. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 43-59 Issue: 1 Volume: 2 Year: 2024 Keywords: Islamic fin-tech; Salam; financial inclusion; disruptive technology; risk mitigation. File-URL: http://www.inderscience.com/link.php?id=139146 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:2:y:2024:i:1:p:43-59 Template-Type: ReDIF-Article 1.0 Author-Name: Nathan Coates Author-X-Name-First: Nathan Author-X-Name-Last: Coates Author-Name: Robert Nydick Author-X-Name-First: Robert Author-X-Name-Last: Nydick Author-Name: D.K. Malhotra Author-X-Name-First: D.K. Author-X-Name-Last: Malhotra Title: Using tree-based models to predict credit risk Abstract: Despite an increase in consumer bankruptcies, the consumer loan industry is increasingly competitive. Financial organisations may find that well-allocated credits are one of the most lucrative sources of income. However, a high degree of risk is associated with this type of banking activity because many incorrect judgements might force the lending institution into bankruptcy. The main goal of credit risk evaluation research is to develop classification rules that properly classify bank clients as either good credit or bad credit loan applicants. This study shows how to use tree-based algorithms, such as decision trees, random forests trees, boosted trees, and XGBoost, to lower the risk of bad loans and find the traits that can help differentiate between a good loan and a bad loan. This will allow loan officers to improve their scoring models by giving those traits more weight when deciding whether to extend loans to borrowers. Lending institutions can protect themselves from legal or regulatory problems by explaining the factors that led them to decide against lending to a potential borrower. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 23-42 Issue: 1 Volume: 2 Year: 2024 Keywords: consumer loans; credit risk; decision trees; bootstrap trees; boosted trees. File-URL: http://www.inderscience.com/link.php?id=139147 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:2:y:2024:i:1:p:23-42 Template-Type: ReDIF-Article 1.0 Author-Name: Vandana Author-X-Name-First: Author-X-Name-Last: Vandana Author-Name: Shatakshi Upadhyay Author-X-Name-First: Shatakshi Author-X-Name-Last: Upadhyay Title: A study on the attitude of Z generation about adopting internet of things in digital era for training and development Abstract: Internet of things has become a buzzword, and becoming part of the life of the people. In this technological era it has gained economic and social significance. Gen Z is the most technologically sophisticated and diversified generation cohort. This study will provide an insight about the attitude of Z Generation for adopting IoT in digital era in their training and development. The research has been carried out on a sample of 248 respondents belonging to the Z generation who have one year of work experience or either employed on internships. A structured questionnaire is used, and exploratory factor analysis, followed by analysis of multiple regression is used to arrive the results. They are also known as hyperlinked generation or iGeneration. The study's results will be exclusive for Gen Z cohort. The present research will find the attitude of Gen Z about IoT and their usage for training and development. This study will contribute to industry practitioners by formulating strategies in industries for training and development through adoption of IoT. Furthermore, the results will also contribute to academic research by providing suggestions and strategies to develop IoT enabled training and development specific to Gen Z. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 60-76 Issue: 1 Volume: 2 Year: 2024 Keywords: internet of things; Z generation; training; development; exploratory factor analysis; multiple regression analysis. File-URL: http://www.inderscience.com/link.php?id=139148 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:2:y:2024:i:1:p:60-76 Template-Type: ReDIF-Article 1.0 Author-Name: Sarah Oliveira Pinto Author-X-Name-First: Sarah Oliveira Author-X-Name-Last: Pinto Author-Name: Vinicius Amorim Sobreiro Author-X-Name-First: Vinicius Amorim Author-X-Name-Last: Sobreiro Title: Note on the curse of dimensionality regarding financial anomaly detection Abstract: The so-called curse of dimensionality represents limitations to the processes of manipulation, interpretation and analysis of high dimensional data, which makes identifying behaviour patterns in cursed dimensional feature spaces complex. Several recently published articles discussed the need to use dimensionality reduction methods when considering the issue of detecting anomalies in financial systems. However, most articles developed models that did not consider real-time data or the continuous generation of information. They also did not directly address how to overcome situations arising from a great volume of data. This short communication reports on articles that used voluminous but static data, and articles that used real-time data to identify anomalies in financial systems but did not significantly address the issues related to dimensionality. Furthermore, this note signals the need for further investigation of how to break the curse of dimensionality through developing financial anomaly detection models to promote better decision support systems. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 77-87 Issue: 1 Volume: 2 Year: 2024 Keywords: curse of dimensionality; high dimensional data; anomaly detection; financial systems. File-URL: http://www.inderscience.com/link.php?id=139149 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:2:y:2024:i:1:p:77-87