Composite forecasting using ridge regression

Abstract

Recent studies have shown that composite Forecasting produces superior forecasts when compared to individual forecasts. This paper extends the existing literature by employing ridge regression techniques in composite model building. Security analysts forecasts may be improved when combined with time series forecasts for a diversified sample of 261 firms with a 1980-1982 post-sample estimation period.

The mean square error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts in an ordinary least squares regression model. This reduction is very interesting when one finds that the univatiate time series model forecasts do not substatially deviate from those produced by ARIMA (0,1,1) processes.

Multicollinearity exists between analyst and time series model forecasts and ridge regression techniques are used to estimate composite earnings models.

Moreover, the estimated mean square ridge regression errors are not statistically different by standardizing different by standardizing the data in ridge regression analysis.

Keywords:

• biased regression
• time series
• portfelic analysis