Financial ratios are routinely used as predictors in modelling tasks where accounting information is required. The purpose of this paper is to discuss such use, showing how to improve the effectiveness of ratio-based models. First, the paper exposes the inadequacies of ratios when used as multivariate predictors and then develops a theoretical foundation and methodology to build accounting-based models. From plausible assumptions about the cross-sectional behaviour of accounting data, the paper shows that the effect of size, which ratios remove, can also be removed by modelling algorithms, which facilitates the discovery of meaningful predictors and leads to markedly more effective models. Experiments verify that the new methodology outperforms the conventional methodology, the need to select ratios among many alternatives is avoided, and model construction is less arbitrary. The new methodology can end the uncritical use of modelling remedies currently prevailing and release the full relevance of accounting information when utilised to support investments and other value-bearing decisions.

This research is supported by the Foundation for the Development of Science and Technology of Macao SAR of China, Project No. 044/2014/A1.

Financial ratios,Accounting-based models,Multivariate models,Predictive models,Value-relevance of accounting information

• Economics and Business - Social Sciences