skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

A nonlinear dynamic approach to cash flow forecasting

Review of quantitative finance and accounting, 2022-07, Vol.59 (1), p.205-237 [Peer Reviewed Journal]

The Author(s) 2022 ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0924-865X ;EISSN: 1573-7179 ;DOI: 10.1007/s11156-022-01066-8

Full text available

Citations Cited by
  • Title:
    A nonlinear dynamic approach to cash flow forecasting
  • Author: Pang, Yang ; Shi, Shimeng ; Shi, Yukun ; Zhao, Yang
  • Subjects: Accounting/Auditing ; Business models ; Cash flow forecasting ; Corporate Finance ; Econometrics ; Economics and Finance ; Finance ; Growth rate ; Longitudinal studies ; Operations Research/Decision Theory ; Original Research ; Panel data ; Performance evaluation ; Power ; Sales
  • Is Part Of: Review of quantitative finance and accounting, 2022-07, Vol.59 (1), p.205-237
  • Description: We propose a novel grey-box model to capture the nonlinearity and the dynamics of cash flow model parameters. The grey-box model retains a simple white-box model structure, while their parameters are modelled as a black-box with a Padé approximant as a functional form. The growth rate of sales and firm age are used as exogenous variables because they are considered to have explanatory power for the parameter process. Panel data estimation methods are applied to investigate whether they outperform the pooled regression, which is widely used in the extant literature. We use the U.S. dataset to evaluate the performance of various models in predicting cash flow. Two performance measures are selected to compare the out-of-sample predictive power of the models. The results suggest that the proposed grey-box model can offer superior performance, especially in multi-period-ahead predictions.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0924-865X
    EISSN: 1573-7179
    DOI: 10.1007/s11156-022-01066-8
  • Source: SpringerOpen
    AUTh Library subscriptions: ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait