Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Firm Earnings Prediction: External and Internal Drivers
Jayantha Wickramasinghe and Ray McNamara
This paper proposes a model for predicting firm earnings based upon the
intrinsic performance capacity of the firm moderated by macroeconomic
influences impacting performance. The intrinsic performance capacity is
deemed to reflect the firm’s structural/executional performance drivers.
While the methodology is yet in its preliminary stage of development and is
subject to the availability of a sufficient sample of time series performance
data, using a pooled cross section of performance data provides usable
estimators for predicting performance amongst sufficiently homogeneous
firms.
The predictions could serve as a benchmark for developing a rational
performance target for a planning period. On a wider level, the residual
derived is a firm and industry specific estimator that can serve as a metric
to evaluate the significance of the response coefficient of an earnings
innovation. Specifically, if the residuals of firms with a management
accounting innovation, such as the balanced scorecard, are compared with
the residual of firms without such an innovation, any significant differences
can be attributed to the innovation.
The paper contributes to the question of the inadequacy of time series
models for earnings prediction. It is proposed that the inadequacy of these
models vis a vis analysts’ forecasts of firm earnings rests with the use of an
earning parameter that factors variables that fail to capture the intrinsic
performance level of the firm moderated by exogenous (macroeconomic)
factors impacting performance.
Keywords: Earnings Prediction, Earnings Innovation, Value Measurement.
Field of Research: Accounting
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Jayantha Wickramasinghe, Massey University, New Zealand,
Email: j.wickramasinghe@massey.ac.nz, Ph. 64-9-414-0800 x9489
Ray McNamara, Bond University, Australia,
Email: rmcnamar@staff.bond.edu.au, Ph. 61-7-5595-2219
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