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http://hdl.handle.net/10071/18189
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Title: Improving the effectiveness of predictors in accounting-based models
Authors: Trigueiros, D.
Keywords: Financial ratios
Accounting-based models
Multivariate models
Predictive models
Value-relevance of accounting information
Issue Date: 2019
Publisher: Emerald
Abstract: 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.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/18189
DOI: 10.1108/JAAR-01-2018-0006
ISSN: 0967-5426
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-60278
Appears in Collections:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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