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Multivariate Data Driven Models for Firm Value Prediction from Small Data Samples

49 Pages Posted: 1 Feb 2008  

Mark B. Barnes

Monash University - Clayton School of IT

Vincent Cheng Siong Lee

Monash University - School of Computing & Information Technology

Date Written: February 1, 2008

Abstract

Firm value is an essential outcome of firm managerial performance measure. From shareholders' and potential investors' perspectives it is an investment decision criterion. This paper studies how firm values are determined by our previous work that explored the variables that drive firm value growth in the Miscellaneous Industrials sector of the Australian Stock Market. We previously studied how the firm's value growth as well as its growth in relation to the changes in the Miscellaneous Industrial Index changed using traditional and artificial intelligent (AI) feature selection techniques. Previously multi-domain models were used to reduce the number of attributes to a manageable number. The primary aim of this study is to investigate the effect of macroeconomic and firm- specific economic factors on firm value growth and relative firm value growth models (also known as multi-domain models) performance. We also explore the capabilities of the multi-domain models to make out-of-sample predictions for firm value growth and relative firm value growth. We find that the macroeconomic and firm specific factors can improve the performance of these multi-domain models, where their raw values generally have a more positive impact on the model performance. It was also found that the price to earning ratio was a consistent positive performer.

Keywords: Financial ratios, macroeconomic variables, multi-domain model, artificial intelligence feature selection techniques, firm value growth, small data sample

JEL Classification: G31, G32

Suggested Citation

Barnes, Mark B. and Lee, Vincent Cheng Siong, Multivariate Data Driven Models for Firm Value Prediction from Small Data Samples (February 1, 2008). Second Singapore International Conference on Finance 2008. Available at SSRN: https://ssrn.com/abstract=1089319 or http://dx.doi.org/10.2139/ssrn.1089319

Mark B. Barnes

Monash University - Clayton School of IT

Clayton
Victoria
Australia

Vincent C. S. Lee (Contact Author)

Monash University - School of Computing & Information Technology ( email )

Australia
+61 3 990 52360 (Phone)

HOME PAGE: http://directory.monash.edu.au/cgi-bin/staffsearch/staffsearch

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