PurposeThe management of liquidity has always been seen as a critical but often ignored issue in finance. Despite the abundance of studies on liquidity management, these studies mainly focus on developed countries and on large firms. Liquidity is critical for the small firm but studies on liquidity management in small and medium enterprises (SMEs) are lacking. The purpose of this paper is to examine the firm-level determinants of liquidity of SMEs in Malaysia.Design/methodology/approachData are collected for a total of 986 small firms in Malaysia from 2011 to 2014, resulting in a total of 2,683 observations. Firm-specific variables and the effect of the economy are considered as the possible determinants of liquidity. Ordinary least squares (OLS) regression analysis with standard errors adjusted for firm-level clustering and quantile regression analysis are used for this purpose.FindingsAnalysis using OLS regression technique indicates that a firm’s profitability, its growth, asset tangibility, size, age and firm status are significant factors in influencing its liquidity decision. Leverage and economic condition are not found to have any significant influence on liquidity. However, quantile regression analysis provides a different picture especially for SMEs with liquidity at the quantile levels ofθ=0.10 and 0.90. Atθ=0.10, only profitability, tangibility and firm status are significant, while atθ=0.90, tangibility, size, firm status and, to some extent, age are significant in influencing liquidity levels.Originality/valueTo the author’s knowledge, this is the first study analyzing the liquidity decision of SMEs in an emerging market such as Malaysia. Most studies on liquidity management of SMEs are focused on developed countries due to data availability but these studies are also only a handful. Additionally, this study uses quantile regression analysis which highlights the need to analyze financial decisions at different levels rather than at the aggregate level as done in OLS regression analysis.
Bayesian semiparametric modeling of response mechanism for nonignorable missing data
