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Sales surprise (SS) is a significant factor in a firm’s inventory turnover (ITO). In order to estimate SS, it is necessary to select an appropriate approach of sales forecasting. The current study’s main purpose is to examine the effects of SS on ITO. The data was gained from the Albertina database from 2017 to 2021, for two sectors: manufacturing and construction. The Czech firms’ panel data was used to estimate sales forecasts by four different methods: (i) sales linear forecast (SLF), (ii) sales change (SCH), (iii) sales growth (SG), and (iv) sales forecast random walk (SRW). The two most accurate methods were chosen to calculate SS: sales surprise linear forecast (SSLF) and sales surprise random walk (SSRW). After estimating four different regression models by employing the fixed-effect panel model, the results show that SSRW is positively correlated with ITO. The sales surprise linear forecast (SSLF) is found to be insignificant. Capital intensity (CI) has a positive impact on ITO; on the other hand, the relationship between gross margin (GMN) and ITO is negative. This is the first research in which SS is measured by four different techniques, and then the two most accurate techniques are used to examine the effects of SS on ITO. Therefore, the findings of the current research will be fruitful for managers, academics, policymakers, and directors of firms to estimate SS using different techniques and to understand the effects of SS on ITO. Hence, the research will be useful to the firms’ management in many contexts.


This article was originally published in Cogent Economics & Finance, volume 11, issue 2, in 2023.

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