Could the analysts¡¦ forecasting be the important investment decision of the naive investors? This is the significant issue of the study. The study shows the following, in the short run, recommendations have information value; in the long run, forecasting earnings have information value. And electronic industry is the most valuable of all industries in the long run and short run. Although recommendations also have information value in the long run, but the industry returns aren¡¦t consistent. In addition, analysts over predict about the forecasting earnings. Finally, the study also shows the following, forecasting price or recommendation has negative coefficient and statistically significant in large scale company. Because large scale companies are more attentive than small scale companies, and information superiority trader already get the information before analysts¡¦ release. So they can trade the stocks before analysts¡¦ release, naive investors can¡¦t get returns with the analysts¡¦ forecasting.

Pricing decision support systems have been developed in order to help retail companies optimise the prices they set when selling their goods and services. This research aims to enhance the essential forecasting and optimisation techniques that underlie these systems. This is first done by applying the method of Dynamic Linear Models in order to provide sales forecasts of a higher accuracy compared with current methods. Secondly, the method of Support Vector Regression is used to forecast future competitor prices. This new technique aims to produce forecasts of greater accuracy compared with the assumption currentlyused in pricing decision support systems that each competitor's price will simply remain unchanged. Thirdly, when competitor prices aren't forecasted, a new pricing optimisation technique is presented which provides the highest guaranteed profit. Existing pricing decision support systems optimise price assuming that competitor prices will remain unchanged but this optimisation can't be trusted since competitor prices are never actually forecasted. Finally, when competitor prices are forecasted, an exhaustive search of a game-tree is presented as a new way to optimise a retailer's price. This optimisation incorporates future competitor price moves, something which is vital when analysing the success of a pricing strategy but is absent from current pricing decision support systems. Each approach is applied to the forecasting and optimisation of daily retail vehicle fuel pricing using real commercial data, showing the improved results in each case.