This paper describes the prediction scheme of stock price by using multiagent systems. Agents predict the stock price according to their strategies which is defined from technical and fundamental parameters such as some index related to the stock price, the currency exchange rate of the Japanese Yen (JPY) against the US Dollar and so on. Agents are randomly generated to construct population and...

Forecasting of oil prices has always been a matter of great importance due to its influence in driving a country’s economy. As a matter of fact, the petroleum industry is considered to be the biggest contributor in the industrial sector in terms of providing raw materials to the other industries and generating revenues. Due to the non-linear and unpredictable nature of the oil prices, a lot of ...

struggling in world's competitive markets, industries are attempting to upgrade their technologies aiming at improving the quality and minimizing the waste and cutting the price. industry tries to develop their technology in order to improve quality via proactive quality control. this paper studies the possible paint quality in order to reduce the defects through neural network techniques in au...

Suppose two Matisse paintings with identical characteristics were put up for sale. However, one of the paintings had previously been sold in a “hot” market for a high price and the other had been sold in a “cold” market for a low price. Would the painting that previously sold in the “hot” market fetch a higher price at auction? Anchoring is one of the most powerful and well-established biases i...

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is tested on stock price prediction of an automotive ma...

In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Fee...

Prediction markets, in which contract prices are used to forecast future events, are increasingly applied to various domains ranging from political contests to scientific breakthroughs. However, the dynamics of such markets are not well understood. Here, we study the return dynamics of the oldest, most data-rich prediction markets, the Iowa Electronic Presidential Election "winner-takes-all" ma...

Stock price, been studied for hundreds of years, is one of the most versatile thus hardly predictable things that is deeply rooted in the modern economy. With the trading frequency reaching sub-second and beyond, more advanced real-time stock price prediction tools would be highly demanded in addition to traditional financial analysis. In this work, we applied SVM and Naïve Bayes algorithms to ...

Bidding in e-auctions in mobile environment is a challenging task as the bidder is mobile that may cause disconnections and may not be acquainted with the emarket environment that hosts multiple concurrent e-auctions. One of the ways to ensure winning a bid is to predict the closing price of considered relevant auctions to decide the bidding strategies. Closing price prediction of an auction de...

This paper proposes an alternative approach based on a genetic algorithm and neural network (GA–NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA–NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann–Whitney test results indicated that the WTI ...