In order to improve the accuracy of forecasting crude oil prices, a new price method is introduced in paper that combination FNN model and stochastic time effective function—namely, WT-FNN model. The keeps track historical values prices predicts future function gives greater weight recent information smaller old information, thus making prediction more reasonable. We selected daily data Brent f...

The goal of this work is to derive models for forecasting the final price of ongoing online auctions. This forecasting task is important not only to the participants of an auction who compete against each other for the lowest price, but also to designers of bidder-side agents. Forecasting prices in online auctions is challenging from a statistical pointof-view because traditional forecasting mo...

Knowledge of the statistical distribution of the prices of emission allowances, and their forecastability, are crucial in constructing, among other things, purchasing and risk management strategies in the emissions-constrained markets. This paper analyzes the two emission permits markets, CO2 in Europe, and SO2 in the US, and investigates a model for dealing with the unique stylized facts of th...

In this paper we examine a representative agent forecasting prices in a first-order self-referential overlapping generations model. We first consider intermediate stage learning, where agents update the forecasting rule every m periods. We show that, in theory and simulations, the learning rule does not converge to the rational expectations equilibrium (REE). We next consider two stage learning...

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. Results of Granger causality tests and tests of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of th...

This paper describes two mutually enhancing technologies that will be used to evolve Bayesian network based forecasting models;. human/artificial cognition and Bayesian networks. A two tiered representation is introduced which mimics the way the human brain is thought to organize itself. This representation can be manipulated using genetic programming techniques to extract both attributes and o...