Stock Market Prediction, COVID Pandemic, and Neural Networks: An Levenberg Marquardt Algorithm Application

Himanshu Goel and Narinder Pal Singh

Business Perspectives and Research, 2026, vol. 14, issue 1, 47-58

Abstract: Stock market forecasting has always piqued the interest of a wide range of investors, practitioners, and researchers. Stock prediction is a complex process due to the presence of an inherent noisy and volatile environment. The stock market’s movement is influenced by a variety of factors. The study of ANN models began in 1969, “when Minsky and Papert discovered two critical flaws in the Artificial Neural Network technique. The first was the machine’s ability to solve complex problems, and the second was the computers’ inability to run large ANN models efficiently†. The study aims to forecast the Nifty 50 using macroeconomic factors as input variables in the two sub-periods, that is, pre-COVID (February 2018–February 2020) and during COVID (March 2020–December 2021). A model trained using the LM algorithm was used for predicting the NSE’s flagship index Nifty 50. The findings reveal that the LM algorithm achieved 95.18% accuracy in predicting the Nifty 50 in the pre-COVID scenario. Whereas during COVID period, the proposed ANN model achieved 94.21% accuracy. The empirical results have important implications for every class of investors, such as FIIs, DIIs, retail investors, and so on.

Keywords: Coronavirus disease; neural networks; LM algorithm; national stock exchange; prediction (search for similar items in EconPapers)
Date: 2026
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/22785337221149817 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:busper:v:14:y:2026:i:1:p:47-58

DOI: 10.1177/22785337221149817

Access Statistics for this article

More articles in Business Perspectives and Research
Bibliographic data for series maintained by SAGE Publications ().