Empirical analysis of daily cash flow time-series and its implications for forecasting

• Francisco Salas-Molina ^[1] ; Juan A. Rodríguez-Aguilar ^[3] ; Joan Serrà ^[4] ; Montserrat Guillen ^[2] ; Francisco J. Martin ^[5]
  1. [1] Universitat de València

     Universitat de València

     Valencia, España

     
  2. [2] Universitat de Barcelona

     Universitat de Barcelona

     Barcelona, España

     
  3. [3] IIIA-CSIC
  4. [4] Telefonica Research
  5. [5] BigML

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• Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 42, Nº. 1, 2018, págs. 73-98
• Idioma: inglés
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• Resumen

  • Usual assumptions on the statistical properties of daily net cash flows include normality, absence of correlation and stationarity. We provide a comprehensive study based on a real-world cash flow data set 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. 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.