Title: Trade credit forecasting: empirical analysis using a ratio targeting approach

Authors: Shame Mugova; Nicola Cucari

Addresses: Durban University of Technology, P.O. Box 1334, Durban 4001, South Africa ' Sapienza University of Roma, Piazzale Aldo Moro, 5, 00185 Roma RM, Italy

Abstract: This study employs a panel data model that uses trade credit's own recent history to predict trade credit levels. A predictive model of trade credit is developed to predict the levels of trade payables and receivables. Previous forecasting techniques do not incorporate the targeting aspect and long period historical data. A target ratio should be set for trade payables and trade receivables to total assets. Trade credit is debt finance which is maintained at a certain ratio to total assets. In this paper, we make use of panel data from 230 non-financial South African listed firms from 2001 to 2013. Firms use trade credit targeting to pursue growth opportunities and their size affects their access to capital. Trade credit's recent history can be used to predict target trade credit levels. The paper makes an original contribution by developing a model to predict the level of trade credit.

Keywords: trade credit; forecasting; historical data; South Africa.

DOI: 10.1504/AAJFA.2022.125064

Afro-Asian Journal of Finance and Accounting, 2022 Vol.12 No.4, pp.413 - 426

Received: 16 Apr 2020
Accepted: 30 Apr 2021
Published online: 25 Aug 2022 *

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