financial markets

Nowadays multilayer perceptrons (MLPs) are one of the most important and widely-used neural networks used as continuous measurable function with a desired accuracy. The second benefit is nonparametric data-driven nature of them which meansmultilayer perceptrons impose few prior assumptions on the underlying process. Being adaptive is the third advantage of MLPs. The adaptation of MLPs implies that in a nonstationary environment the accuracy and robustness of results are still countable. Utilizing fewer parameters is the fourth benefit of MLPs. Despite all these unique advantages of multilayer perceptrons, they suffer from some limitations such as negative relationship between number of inputs and the achieved performance, though using hybrid methods to overcome the limitations by means of a method alone and improving forecasting performance is achievable. Literature review suggests that by utilizing disparate and unrelated methods, we can obtain a new hybrid scheme capable of less variance or error. Hybridization of dissimilar methods can reduce the risk of using an inappropriate method. Usually, this is done based on this fact that the underlying process cannot easily be determined. The motivation behind using hybrid method is two folds: either single method cannot identify the true data generating process or cannot identify all the characteristics of the time series. In this paper, a new hybrid method of multilayer perceptrons is proposed which uses the self-organizational maps. The self-organizational maps are one of the most accurate tools in recognizing and analyzing the nonlinear multidimensional spaces. In the proposed method, inputs of the multilayer perceptron are firstly clustered by using a self-organizational map, and then variables in each cluster are combined together according to their effectiveness values. Empirical results of steel price forecasting in TehranMetal Exchange indicate that the efficiency of the proposed method is comparable to other methods.

Forecasting is one of the effective tools for planning and establishing the financial strategies. Forecasting accuracy is also one of the most important factors in choosing the forecasting method. Nowadays, despite the numerous forecasting models available, accurate forecasting is not yet a simple task, especially in financial markets. Thus, different models have been combined together in order to achieve more accurate results. Combining different models or using hybrid forecasting models is a common way for overcoming deficiecies of the single models and improving their performance. In the literature, several hybrid models of multilayer perceptrons have been proposed in order to overcome the disadvantages of these models. In this paper, a new hybrid model of multilayer perceptrons is proposed using probabilistic neural classifiers. The proposed model improves the performance of the multilayer perceptrons using the unique advantages of the probabilistic neural classifiers in detecting the break points and better and more complete modeling of the specific patterns in the under-study time series. Empirical results of exchange rate forecasting indicate the efficiency of the proposed model in comparison with other models.