چکیده:
Uncertainty in the capital market means the difference between the expected values and the amounts that actually occur. Designing different analytical and forecasting methods in the capital market is also less likely due to the high amount of this and the need to know future prices with greater certainty or uncertainty. In order to capitalize on the capital market, investors have always sought to find the right share for investment and the right price to buy and sell, and so all the predicted models always seek to answer the three basic questions, i.e., which share, to what extent When and at what price to buy or sell. Before answering the answers given to these questions, you have to answer a more serious question. Including whether forecasting financial markets is possible. Accordingly, in this research, using data mining, we proposed a method for predicting changes in the total stock index of Tehran stock exchanges. The purpose of this research is in the field of applied research. In terms of its implementation, the research is based on a causal research that is carried out using a data collection database. Based on the results obtained from this study on the best decision tree algorithm with respect to the accuracy of 94% of the C & R Tree algorithm, it can be said that this algorithm can be better than predicting stock price changes. Also, using decision tree can also predict changes in the price of the payment.
خلاصه ماشینی:
Forecasting of Tehran Stock Exchange Index by Using Data Mining Approach Based on Artificial Intelligence Algorithms Mohammad Mahmoodi*a, Akbar Ghasemib a Assistant professor, Faculty of management, Accounting department, Islamic Azad University, Firoozkouh branch, Iran b Msc.
Accordingly, in this research, using data mining, we proposed a method for predicting changes in the total stock index of Tehran stock exchanges.
The results of this research indicate that fuzzy neural networks have superiority over the ARIMA method and have the features of fast and timely uniqueness exceeding the stock price index is appropriate Charkha (2008) proved that neural networks perform better than predictive value in statistical methods.
In addition to the superiority of the technique introduced by the authors, the model became more complex than statistical methods, and so neural networks can be used as an alternative to predicting stock prices on a daily basis.
Based on the results, the author concludes that the proposed method can be effective in identifying and eliminating turmoil in stock prices and improving the efficiency of neural networks.
(2011) investigated the prediction of moving the stock price index of the Istanbul Stock Exchange with neural-fuzzy and SVM models, and the daily data from 1997 to 2007 along with 10 technical indicators as input variables were used.
In this research, using data mining, we proposed a method for predicting changes in the total index of Tehran Stock Exchange shares.