Robust Regression Analysis in Analyzing Financial Performance of Public Sector Banks: A Case Study of India

Asif Pervez () and Irfan Ali ()
Additional contact information
Asif Pervez: Jamia Millia Islamia
Irfan Ali: Aligarh Muslim University

Annals of Data Science, 2024, vol. 11, issue 2, No 11, 677-691

Abstract: Abstract Regression analysis is a statistical method to analyze financial data, commonly using the least square regression technique. The regression analysis has significance for all the fields of study, and almost all the fields apply least square regression methods for data analysis. However, the ordinary least square regression technique can give misleading and wrong results in the presence of outliers and influential observations in the data. Robust estimation is a statistical method to analyze such financial data with outliers. It is an alternative method for the least square regression for such data. It is necessary to elaborate on the applications of the robust regression model in analyzing real-world financial data that do not fulfil the assumptions of most statistical methods of data analysis to get reliable results. Public sector banks are the backbone of the Indian financial system. The present study analyzed the financial performance of public sector banks in India by applying a robust linear regression technique for more reliable outcomes. Twenty-one public sector banks were selected to study based on the importance of public sector banks in India.

Keywords: Financial data; Financial analysis; Bank performance; Robust regression analysis; Robust estimation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-022-00427-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:aodasc:v:11:y:2024:i:2:d:10.1007_s40745-022-00427-3

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-022-00427-3

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().