Trends in statistically based quarterly cash-flow prediction models

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

This paper provides a succinct review and synthesis of the literature on statistically based quarterly cash-flow prediction models. It reviews extant work on quarterly cash-flow prediction models including: (1) complex, cross-sectionally estimated disaggregated-accrual models attributed to Wilson (1986, 1987) and Bernard and Stober (1989), (2) parsimonious ARIMA models attributed to Hopwood and McKeown (1992), (3) disaggregated-accrual, time-series regression models attributed to Lorek and Willinger (1996), and (4) parsimonious ARIMA models with both adjacent and seasonal characteristics attributed to Lorek and Willinger (2008, 2011). Due to the unavailability of long-term cash-flow forecasts attributed to analysts, increased importance has been placed upon the development of statistically based cash-flow prediction models given their use in firm valuation. Specific recommendations are also provided to enhance future research efforts in refining extant statistically based quarterly cash-flow prediction models.

Original languageEnglish (US)
Pages (from-to)145-151
Number of pages7
JournalAccounting Forum
Volume38
Issue number2
DOIs
StatePublished - Jun 2014

Keywords

  • ARIMA
  • Cross-sectional estimation
  • Statistically based cash-flow prediction models
  • Time-series estimation

ASJC Scopus subject areas

  • Accounting
  • Finance

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