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- 저자 : Hafeez Ur Rehman Zubair PANHWAR (검색결과 1 건)
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Financial Distress Prediction Using Adaboost and Bagging in Pakistan Stock Exchange
Fayaz Hussain TUNIO,Yi DING,Amad Nabi AGHA,Kinza AGHA,Hafeez Ur Rehman Zubair PANHWAR 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.1
Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies’ bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company’s default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.
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