Abstract
Purpose: This study aims to illustrate the relationship between the liquidity of a stock and the return distribution of all companies listed on the Indonesian Stock Exchange. Design/methodology/approach: The correlation between stock liquidity and return distribution, represented using skewness and kurtosis methods, was determined by the author through two-way tests using line regression (fixed cross-section regression) and machine learning (random forest regressor) for Indonesia data Stock exchange from 2014 to 2023. Findings: The results indicate a connection between stock liquidity and return distribution. When evaluating stock liquidity, the zero return days technique gives a more significant relationship, while the kurtosis of return method gives a more significant return distribution. It is impossible to determine with absolute certainty whether the correlation is positive or negative; it depends on how stock liquidity and return distribution are measured. Research limitations/implications: The correlation between stock liquidity and return distribution cannot be characterized as an absolute relationship due to the intricacy and variety of approaches used to measure it. Practical implications: The kurtosis of return has a better relationship with stock liquidity, and “zero return day” and “relative spread” are still more strongly correlated with the return distribution. Originality/value: As the first study to use machine learning and linear regression to examine the relationship between stock liquidity and return distribution, this paper expands the body of knowledge. Second, it examines which return distribution – skewness or kurtosis – has a stronger correlation with stock liquidity. Third, we examine which liquidity metrics—relative spread, Amihud liquidity, price impact ratio, and zero return days—are most related to the return distribution.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.