A nonlinear dynamic approach to cash flow forecasting

Pang, Y., Shi, S., Shi, Y. and Zhao, Y. (2022) A nonlinear dynamic approach to cash flow forecasting. Review of Quantitative Finance and Accounting, 59(1), pp. 205-237. (doi: 10.1007/s11156-022-01066-8)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shi, Dr Yukun and Zhao, Dr Yang and Pang, Mr Yang
• Authors: Pang, Y., Shi, S., Shi, Y., and Zhao, Y.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance; College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Review of Quantitative Finance and Accounting
• Publisher: Springer
• ISSN: 0924-865X
• ISSN (Online): 1573-7179
• Published Online: 16 May 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Review of Quantitative Finance and Accounting 59(1): 205-237
• Publisher Policy: Reproduced under a Creative Commons licence