Discovering the optimal set of ratios to use in accounting-based models

• Autor(a) principal: Trigueiros, D.
• Data de Publicação: 2018
• Outros Autores: Sam, C.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/28947
• topic: Knowledge extraction; Financial analysis; Financial ratios; Financial technology; Fintech; Accounting models; Bankruptcy prediction; Financial misstatement detection; Earnings forecasting

Ratios are the prime tool of financial analysis. In predictive modelling tasks, however, the use of ratios raises difficulties, the most obvious being that, in a multivariate setting, there is no guarantee that the collection of ratios eventually selected as predictors will be optimal in any sense. Using, as starting-point, a formal characterisation of cross-sectional accounting numbers, the paper shows how the multilayer perceptron can be trained to create internal representations which are an optimal set of ratios for a given modelling task. Experiments suggest that, when such ratios are utilised as predictors in well-known modelling tasks, performance improves on that reported by the extant literature.