fuzzy inference systems

Investing on the stock exchange, as one of the financial resources, has always been a favorite among many investors. Today, one of the areas, where the prediction is its particular importance issue, is financial area, especially stock exchanges. The main objective of the markets is the future trend prices prediction in order to adopt a suitable strategy for buying or selling. In general, an investor should be predicted the future status of the time, the amount and location of his assets in a way that increases the return on his assets. Stock price prediction is one of the most challenging topics in the field of forecasting, which has attracted many attentions from researchers. The various factors of the markets have caused the situation that they always have a dynamic and complex process. Therefore, researchers have been determined to look for new prediction methods of stock price, which will reduce the instability and complexity of the markets. In fact, the most of recent studies have shown that the stock market is a nonlinear, dynamic, and non-parametric system that is affected by various economic factors. The applications of artificial intelligence and machine learning techniques to identify the relationship between the factors and stock price exchanges can be organized in seven major groups such as neural networks and deep learning, support vector machine, decision tree and random forest, k nearest neighbor, regression, Bayesian networks and fuzzy inference-base methods. Due to the mentioned prediction methods have their own challenges, hydridizations of the meta-heuristic algorithms and the methods were applied to stock price prediction. In this paper, a new hybridization of Fuzzy Inference System and a novel modified Fuzzy Imperialist Competitive Algorithm (FICA+FIS) are proposed to stock price prediction. To achieve this aim, two Fuzzy Inference Systems are designed to tuing the ICA’s parameters based on three effective factors in search strategy and to predict stock price based on 10 effective economic factors. The candidate fuzzy rules set of the inference engine is obtained by the FICA for the second FIS and six fuzzy rules of the first FIS are designed based on the ICA’s behaviour. The FICA+FIS has 10 inputs of the stock price variables including the lowest stock price, the highest stock price, the initial stock price, the trading volume, the trading value, the first market index of the trading floor, the total market price index, the dollar exchange rate, the global price per ounce of gold, the global oil price, and its output is also the stock price. The inputs and output variables consist of three linguistic vairables such as Low, Medium, and High with triangular membership functions. Each country (search agent) of the FICA contains information on all the fuzzy rules of the inference engine attributed to the country and has r×12 elements, where r is the number of fuzzy rules. The FICA’s objective function is the mean square error (MSE) to evaluate the power of each country. A challenge of the ICA is the proper tuning paprameters such as the Revolution Probability (Prevolve), Assimilation Coefficient (Beta) and the Colonies Mean Cost Coefficient (zeta), which has a great impact on the efficiency of the algorithm (precision and time of access to solution). These parameters are usually constant and according to different problems, they have different values and are given experimentally. In this paper, the parameters are tuned based on the number of iterations that the best objective function value has not improved (UN), the number of imperialist (Ni) and the current number iteration (Iter). To this aim, a FIS is designed based on six fuzzy rules that UN, Ni and Iter are its input variables and Prevolve, Beta and zeta are its output variables. To analyze the efficiency of the FICA+FIS as a case study, six datasets are collocted from six companies which were active between 1389 to 1394 in Tehran Stock Exchange such as Pars Oil, Iran Khodro, Motogen, Ghadir, Tidewater and Mobarakeh. The information of around 2000 days are collected for each company and the data are divided to train and test data based on cross validation 10-fold. To compare the performance of the FICA+FIS, two groups of stock price prediction methods were implemented. In the first group, the fuzzy rules of the FIS’s engine to stock price prediction are obtained by the classic draft of the Imperialist Competitive Algorithm (ICA+FIS), the Genetic Algorithm (GA+FIS) and the Whale Optimization Algorithm (WOA+FIS), which are used to compare with the FICA. The second group includes classic stock price prediction methods such as multi-layered neural network (NN), support vector machine (SVM), CART decision tree (DT-CART), random forest (RF) and Gaussian process regression (GPR), which are used to compare with the FICA+FIS. The experimental results show that first, the improved fuzzy draft of the ICA performed better than its classic draft, the GA and the WOA, and second, the performance of the FICA FIS is better than other investigated algorithms in both training and testing phases, although the DT is a competitor in the training phase and the RF is a competitor in the test phase on some datasets.