Robust Liquidity Planning Under Cash-Flow Uncertainty: Statistical Forecasting and Optimization Using Walmart's Public Financial Disclosures

Corporate treasurers must decide how much liquidity to hold against cash flows that are seasonal and uncertain. We study this decision for Walmart Inc. using 70 quarters of first-reported data from SEC filings (April 2009 to July 2026). We forecast cash flow after capital expenditure and dividends with simple seasonal models, convert the forecast error distribution into liquidity buffers using chance-constrained, cost-minimizing and robust rules, and evaluate them in a sequential out-of-sample backtest with an expanding window over 30 quarters (April 2019 to July 2026). Walmart's cash flow is strongly seasonal, and its short-term borrowing peaks in the third fiscal quarter, when cash flow before financing is typically negative. Buffers calibrated on history nonetheless under-covered after 2019: a buffer targeting a 5% breach rate was breached in 20% of test quarters (6 of 30; Kupiec p = 0.004), because the standard deviation of forecast errors rose by roughly 80% (variance ratio 3.30). The buffers did not significantly beat a fixed rule of 3% of quarterly cost of sales. A post-hoc adaptive recalibration cut breaches to 13% at a larger average buffer, but cost differences were not statistically significant. For Target and Costco, whose series were not extended beyond 2025, history-based buffers held (breach rates 7.7% and 3.8%). We conclude that statically calibrated liquidity buffers are fragile under regime shifts, and that the benefit of flexible buybacks depends on management actually using them, which Walmart's history does not show.