Deep learning is an exciting topic. It has been utilized in many areas owing to its strong potential. For example, it widely used the financial area which vital society, such as high-frequency trading, portfolio optimization, fraud detection and risk management. Stock market prediction one of most popular valuable finance. In this paper, proposes a stock model using Generative Adversarial Netwo...

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 ...

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...