  Cash managers make daily decisions based on predicted monetary inflows from
debtors and outflows to creditors. Usual assumptions on the statistical
properties of daily net cash flow include normality, absence of correlation and
stationarity. We provide a comprehensive study based on a real-world cash flow
data set from small and medium companies, which is the most common type of
companies in Europe. We also propose a new cross-validated test for time-series
non-linearity showing that: (i) the usual assumption of normality, absence of
correlation and stationarity hardly appear; (ii) non-linearity is often
relevant for forecasting; and (iii) typical data transformations have little
impact on linearity and normality. Our results provide a forecasting strategy
for cash flow management which performs better than classical methods. This
evidence may lead to consider a more data-driven approach such as time-series
forecasting in an attempt to provide cash managers with expert systems in cash
management.
