Simulating management's earnings-per-share forecasts

Article Abstract:

This study attempts to simulate management's earnings-per-share forecasts using six naive time series models. The results indicate two (random walk and random walk with a drift) of the six models were significantly more accurate than the other four models in simulating management's earnings forecast. Both exhibited a tendency to consistently underforecast earnings, and it was hypothesized that this could be due to an inherent bias in the data. The results for the remaining four models were not as favorable. The two submartingales and the simple linear trend models exhibited a high degree of variability in accuracy and significant biases based on the magnitude of the forecast. The arithmetic average model appeared to be unbiased but exhibited a high degree of variability. The results of the study indicate that it may be possible to simulate management's earnings forecasts using relatively naive mathematical models. (Reprinted by permission of the publisher.)

Profits, Mathematical models, Forecasting, Technical, Comparison, Financial Analysis Software, Management Applications, Profit, Time Series

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On simulating a die toss with coin flips

Article Abstract:

In this paper a general method for the generation of discrete uniform random deviates from random binary digits is examined. A measure of efficiency is developed and an improvement on the fundamental technique is also explored. (Reprinted by permission of the publisher.)

Numerical analysis, Product introduction, Mathematics, New Technique, Random Number Generation, Binary Number System, technical

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