Financial Distress Prediction Using Adaboost and Bagging in Pakistan Stock Exchange

• 발행기관: 한국유통과학회
• 저자명: Fayaz Hussain TUNIO Yi DING Amad Nabi AGHA Kinza AGHA Hafeez Ur Rehman Zubair PANHWAR
• 간행물 정보: 『The Journal of Asian Finance, Economics and Business(JAFEB)』Vol. 8 No.1, 665~673쪽, 전체 9쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2021.01.30

영문 초록

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.

목차

1. Introduction

2. Literature Review

3. Methods

4. Empirical Experiment

5. Conclusion

References

키워드

Classifier Combination Data Mining Default Forestalling Firm-Level Variables