Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2018

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

  • Identification data

    Identifier: RP:2474
    Authors:
    Martin, Francisco J.Guillen, MontserratSerrà, JoanRodríguez-Aguilar, Juan A.Salas-Molina, Francisco
    Abstract:
    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.
  • Others:

    URV's Author/s: Martin, Francisco J. Guillen, Montserrat Serrà, Joan Rodríguez-Aguilar, Juan A. Salas-Molina, Francisco
    Keywords: Statistics, forecasting, cash flow, non-linearity, time-series
    Abstract: 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.
    Journal publication year: 2018
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article
  • Keywords:

    Statistics, forecasting, cash flow, non-linearity, time-series
  • Documents:

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