Cash management models determine policies based either on the statistical properties of daily cash flow or on forecasts . Usual assumptions on the statistical properties of daily  cash flow include normality, independence and stationarity. Surprisingly, little empirical evidence confirming these assumptions has been provided. In this work, we provide a comprehensive study on 54 real-world daily cash flow data sets, which we also make publicly available. Apart from the previous assumptions, we also consider linearity, meaning that cash flow is proportional to a particular explanatory variable, and we propose a new cross-validated test for time series non-linearity . We further analyze the implications of all aforementioned assumptions for forecasting, showing that: (i) the usual assumption of normality, independence and stationarity hardly appear; (ii) non-linearity is often relevant for forecasting; and (iii) common data transformations such as outlier treatment and Box-Cox have little impact on linearity and normality. Our results highlight the utility of non-linear models as a justifiable alternative for time series forecasting .
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 .
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management models determine policies based either on the statistical properties of daily cash flow or on forecasts
managers make daily decisions based on predicted monetary inflows from debtors and outflows to creditors
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net
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independence
absence of correlation
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Surprisingly, little empirical evidence confirming these assumptions has been provided. In this work, we
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on 54
based on a
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daily
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sets, which we also make publicly available. Apart from the previous assumptions, we also consider linearity, meaning that cash flow is proportional to a particular explanatory variable, and we
set from small and medium companies, which is the most common type of companies in Europe. We also
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time series
time-series
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. We further analyze the implications of all aforementioned assumptions for forecasting,
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independence
absence of correlation
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common data transformations such as outlier treatment and Box-Cox
typical data transformations
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highlight the utility of non-linear models as a justifiable alternative for time series forecasting
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