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Export Reference (APA) 
Trigueiros, D. & Sam, C. (2018). Discovering the optimal set of ratios to use in accounting-based models. International Journal of Society Systems Science. 10 (2), 110-131
Export Reference (IEEE) 
D. M. Trigueiros and C. Sam,  "Discovering the optimal set of ratios to use in accounting-based models", in Int. Journal of Society Systems Science, vol. 10, no. 2, pp. 110-131, 2018
Export BibTeX 
@article{trigueiros2018_1716145368774,
	author = "Trigueiros, D. and Sam, C.",
	title = "Discovering the optimal set of ratios to use in accounting-based models",
	journal = "International Journal of Society Systems Science",
	year = "2018",
	volume = "10",
	number = "2",
	doi = "10.1504/IJSSS.2018.10013669",
	pages = "110-131",
	url = "http://www.inderscience.com/jhome.php?jcode=ijsss"
}
Export RIS 
TY  - JOUR
TI  - Discovering the optimal set of ratios to use in accounting-based models
T2  - International Journal of Society Systems Science
VL  - 10
IS  - 2
AU  - Trigueiros, D.
AU  - Sam, C.
PY  - 2018
SP  - 110-131
SN  - 1756-2511
DO  - 10.1504/IJSSS.2018.10013669
UR  - http://www.inderscience.com/jhome.php?jcode=ijsss
AB  - 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.
ER  -