Time-series Models Forecasting Performance in the Baltic Stock Market

Abstract

Contradicting evidence on time-series and financial analysts’ forecasting performance calls for further research in emerging markets. Motivation to use time-series models rather than analysts’ forecasts stems from recent research that reports time-series predictions to be superior to analysts’ forecasts in predicting earnings for longer periods and for small firms that are hardly followed by financial analysts, especially in emerging markets. The paper aims to explore time-series models performance in forecasting quarterly earnings for Baltic Firms in 2000-2009. The paper uses simple and seasonal random walk models with and without drift, Foster’s, Brown-Rozeff’s and Griffin-Watts’ models to forecast quarterly earnings. It also employs the firm-specific Box-Jenkins methodology to perform time-series analysis for individual firms. Forecasting performance of selected models is compared on the basis of goodness-of-fit statistics. The paper finds that naive time-series models outperform premier ARIMA family models in terms of mean percentage errors and average ranks. The findings suggest that investors use naive models to form their expectations.