It is very necessary to research the fluctuation of agricultural products prices as well as the data character. In this paper, the wholesale price of agricultural product is considered. The monofractal analysis and multifractal analysis methods are introduced to study wholesale price. We select the appropriate algorithm to calculate the fractal characteristics of the celery wholesale price. Res...

The current gold market shows a high degree of nonlinearity and uncertainty. In order to predict the gold price, Empirical Mode Decomposition (EMD) was introduced into Support vector machine (SVM). Firstly, we used the EMD method to decompose the original gold price series into a finite number of independent intrinsic mode functions (IMFs), and then grouped the IMFs according to different frequ...

Laboratory markets are conducted to evaluate the effects of consumer search costs on market performance. The primary research goal is to assess the behavioral relevance of Diamond’s (1971) paradoxical conclusion that the injection of a small consumer search cost alters the equilibrium price prediction from competitive to monopoly levels. Although monopoly prices are not consistently observed, w...

Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attemp...

We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies’ stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This...

This study provides a principal component analysis-fuzzy-support vector regression model for stock price prediction. Stocks with similar historical trends are selected using principal component analysis. Fuzzy information granulation is performed to construct a probability density for stock prices. Support vector regression is implemented to generate a regression function for future price predi...

technical analysis is the forecasting of future price movement based on an examination of past prices. some scientist found that the study of historical prices cannot predict future prices. in this research we intend to study which technical analysis tool is better for prediction of future price movement, for this purpose we are studying two the most strongest technical analysis tools is called...

Considering the fact that natural gas is a widely used energy source,  the prediction of its consumption can be useful (Derek LAM, 2013). As Iran has one of the largest gas reserves in the world, its consumption in the country can affect the worldwide price of gas, Therefore, the current research is useful both from economic and environmental point of view. ...

The problem of optimal portfolio selection has attracted a great attention in the finance and optimization field. The future stock price should be predicted in an acceptable precision, and a suitable model and criterion for risk and the expected return of the stock portfolio should be proposed in order to solve the optimization problem. In this paper, two new criterions for the risk of stock pr...