Earticle

초록

영어

In recent decades, machine learning (ML) algorithms has gained wide popularity in the finance literature. The goal of this research is to exploit machine learning techniques in order to analyze the effect of exchange-traded fund (ETF) illiquidity on tracking errors. We demonstrate the superior performance of the machine learning models – Random Forest and Gradient Boosting Decision Tree, in particular - over traditional linear models in predicting U.S. ETF’s tracking errors. Moreover, our variable importance analysis suggests that the features such as underlying assets based on U.S. assets (Invested in US Asset) and expense ratio (Expense Ratio), are two key factors in the determination of predicting the tracking errors on the ETF illiquidity. Finally, we further conduct SHAP (Shapley Additive exPlanations) technique in order to observe the impact of a particular variable(feature) on the difference between the considered- and average predictions of our machine learning models. Our results indicate that the most relevant variable is Invested in US Asset, which is in align with the previous importance analysis.

목차

ABSTRACT
I. Introduction
II. Literature review
2-1. The Effect of Illiquidity of ETFs on Tracking Error
2-2. The Machine Learning approach to financial market (ETF)
III. Data and Methodology
IV. Empirical analysis
4-1. The main analysis
4-2. Shapley value
V. Discussion
VI. Conclusion
References
Appendix

키워드

저자

• Jin-Hyung Cho [ Research Fellow (Ph.D.), Kakao ] Corresponding Author
• Gun-Hee Lee [ College of Economics and Finance, Hanyang University ] First Author
• Won-Eung Lee [ College of Economics and Finance, Hanyang University ] Co-Author
• Bong-Jun Kim [ Viterbi School of Engineering, University of Southern California ] Co-Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재무학회 학술대회

• 간기

  부정기

• 수록기간

  2006~2024

• 십진분류

  KDC 325 DDC 330