Forthcoming and Online First Articles

International Journal of Business Intelligence and Systems Engineering

International Journal of Business Intelligence and Systems Engineering (IJBISE)

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International Journal of Business Intelligence and Systems Engineering (3 papers in press)

Regular Issues

  • A Machine Learning perspective of the Impact of COVID 19 on the Indian Stock Market   Order a copy of this article
    by Jared Dominic Fernandez, Arya Kumar 
    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 LASSO (Least Absolute Shrinkage and Selection Operator) 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 GRU (Gated Recurrent Unit) model is able to capture the stock market dip and gradual recovery much better than an LSTM (Long Short-Term Memory) 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.
    Keywords: Stock Market Prediction; Machine Learning; Long Short-Term Memory (LSTM); Gated Recurrent Unit (GRU); Variable Perturbation; COVID 19; NIFTY; Indian Stock Market.

  • E- Salam Finance for Risk Management Islamic Fin-Tech Mobile Based Micro-finance Approach   Order a copy of this article
    by Omer Hag Hamid 
    Abstract: Purpose: 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 Design/methodology/approach 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. Findings: 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 Fintech. Third, the paper gives MFIs the chance to offer positive social impacts through financial services. Practical implications: This paper offers a mobile application approach for microfinance Social implications: Can used to satisfy small business owners in agricultural activities; Originality: The first paper provides the E-Salam finance structure
    Keywords: Islamic Fintech; Salam; financial inclusion; disruptive technology; risk mitigation,.

  • USING TREE-BASED MODELS TO PREDICT CREDIT RISK   Order a copy of this article
    by Nathan Coates, Robert Nydick, D.K. Malhotra 
    Abstract: Despite an increase in consumer bankruptcies, the consumer loan industry is increasingly competitive. Financial organizations 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.
    Keywords: consumer loans; credit risk; decision trees; bootstrap trees; boosted trees.
    DOI: 10.1504/IJBISE.2022.10054394