Forthcoming and Online First Articles

International Journal of Business Intelligence and Systems Engineering

International Journal of Business Intelligence and Systems Engineering (IJBISE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Business Intelligence and Systems Engineering (1 paper 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.