An application of interval methods to stock market forecasting

Stock market forecasting has been a challenging financial research topic for decades. In the literature, there are numerous results based on point methods. However, poor forecasting quality has been a continuous problem. Motivated by the fact that financial data varies within intervals, we apply interval methods on a well known stock pricing model [ 3] to predict stock market variability as intervals. Empirical results obtained with a few different approaches in this paper consistently suggest that interval forecasts have better overall quality than traditional point forecasts.