2016/11/15 by Francisco Salas‐Molina, Juan A. Rodríguez-Aguilar, Salas-Molina, Francisco +7
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Capital Investment and Risk Analysis #Complex Systems and Time Series Analysis #FOS: Economics and business #Financial Reporting and Valuation Research #Forecasting Techniques and Applications #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.1611.04941
openalex publication_date 2016/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.