نوع مقاله : پژوهشی
نویسندگان
1 گروه مدیریت، دانشکده اقتصاد و علوم اجتماعی. دانشگاه شهید چمران اهواز. ایران
2 گروه مدیریت. دانشکده اقتصاد و علوم اجتماعی. دانشگاه شهید چمران اهواز. اهواز. ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The purpose of the current research is to evaluate meta-innovative methods to predict stock price behavior and introduce the most efficient method in the Iranian stock market. Due to the uncertainty in the field of investment and the multitude of variables, investors turn to the forecasting method, by which their estimates are close to reality and their errors are reduced.. In this study, the stock prices of 5 listed companies in the base metals index of Tehran Stock Exchange in the period 1396 to 1398 were predicted. For this purpose, the optimal variables are selected from among 9 primary and widely used variables using feature selection methods, super-innovative algorithms of Harris and Wall's algorithms and then using Back propagation neural networks, radial base neural network and neural network with time delay Stock prices were predicted. The results showed that WOATD, HHOTD, HHOTD, HHOTD and HHORBF are the best models in predicting the stock prices of Famli, Zangan, Faravar, Fasmin and Foolad, respectively. It worked better. According to the results, HHOTD model is more efficient than other models.
The findings show that in the symbol of Zangan, the combined models HHOBP, WOARBF and HHOTD have the highest R2 coefficient, respectively. In terms of MSE and MAE criteria, the HHOTD model has the lowest error rate and the lowest absolute average of the difference between the actual and predicted data, which shows that in this model, the predicted values are closer to the actual values. According to the Hitrate criterion, the HHOTD model has the highest correct estimation (89%) compared to the rest of the models. With these results, it can be concluded that Harris Hawk algorithm has the highest accuracy and the neural network with time delay has high efficiency.
Since linear models do not have the ability to understand and extract non-linear patterns such as stock price time series, the goal in this research was to combine artificial neural networks and meta-heuristic algorithms to accurately predict stock price trends. In this research, first, the optimal variables were selected using feature selection methods, meta-heuristic algorithms of Harris hawk and Whale. Then, by using neural networks after propagation of error, radial basis neural network and neural network with time delay, the stock price was predicted.
Considering all the findings, it is suggested that the Harris's hawk evolutionary method is superior to the whale's evolutionary algorithm. Proper navigation of the problem space and finding features is the reason for the superiority of Harris's hawk results compared to whale's algorithm. It was also shown in this research that networks with time delay have higher efficiency. Findings indicate that the hybrid method of Harris hawk and neural network with time delay is the most effective. It is suggested to use a hybrid model of post-error propagation neural network, radial base neural network and time delay neural network with evolutionary algorithms such as genetics in future research. Also, it is suggested to use other predictive algorithms such as support vector regression in combination with neural network with time delay.
کلیدواژه‌ها [English]
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