Financial Disclosures by Using Machine Learning Analysis

Sarea A., Subramanian S., Alareeni B., Shaikh Z. H., Hawaldar I. T., Elshaker A. H.

International Conference on Business and Technology, ICBT 2020, İstanbul, Turkey, 14 - 15 November 2020, pp.52-60 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1007/978-3-030-69221-6_5
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.52-60
  • Middle East Technical University Affiliated: Yes

Abstract

© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.The effect of Age, Liquidity, Leverage, Size, Industry and Profitability in listed firms in Bahrain bourse is examined in this research paper and machine learning regression techniques in Python programming analysis is adopted to measure the effect of these factors (Age, Liquidity, Leverage, Size, Industry and Profitability) on the Electronic Financial Disclosure (EFD) through the Website of each firm listed in Bahrain Bourse (BB). Listed firms in Bahrain Bourse (BB) during 2017 is taken as sample size in this research. The main finding is that profitability factor is having highest impact on the level of Electronic Financial Disclosure (EFD) which has been tested and predicted using KNN, Polynomial, Multiple Linear Regression techniques. The implication of this study helps firms in Bahrain to use machine learning techniques to predict the effect of the firm characteristics and the level of Financial Disclosure.