FORECASTING CORPORATE CASH HOLDINGS IN VIETNAM: COMPARING MACHINE LEARNING MODELS AND THE IMPACT OF THE WORLD UNCERTAINTY INDEX
TL;DR
This study forecasts the cash holdings (CASH) of listed companies in Vietnam based on data from 2018-2025 using a machine learning approach and identifies HistGradientBoosting as the optimal model, demonstrating strong non-linearity in liquidity management behavior in the Vietnamese market.
Abstract
This study forecasts the cash holdings (CASH) of listed companies in Vietnam based on data from 2018-2025 using a machine learning approach. Using panel data with 4,752 observations, the study compares seven algorithms and identifies HistGradientBoosting as the optimal model (R2 approx. 0.7145), far surpassing traditional linear regression (R2 approx. 0.28). This result demonstrates strong non-linearity in liquidity management behavior in the Vietnamese market. Using SHAP and Permutation Importance, the study successfully elucidates the underlying predictive mechanisms. The main drivers are net working capital (NWC) and the current ratio (CR). Notably, short-term debt (STD), interest expense (IE), and the World Uncertainty Index for Vietnam (WUIVN) consistently showed positive effects, strongly reinforcing the Precautionary Motive. The study also found a moderating effect of size, indicating that small businesses are more sensitive and vulnerable to systemic shocks. The application of machine learning models not only improves forecasting accuracy but also provides a transparent view of corporate financial strategies in an uncertain economic environment.