چكيده به لاتين
One of the main concerns of stock market investors is forecasting stock prices. Stock market decision making is very difficult and important due to the complex behavior and unstable nature of the stock market. As a result, discovering valuable information produced by the market is essential. All investors usually need to find a better way to predict future stock price behavior that will help them determine the best time to buy or sell stocks to make more profit on their investments.
In fact, forecasting stock returns as well as the financial condition of the company is very necessary to advance the interests of investors to invest with better confidence. To provide a forecast analysis, in this study, the final price was studied in 3 stock price data based on different groups from 2 approaches; The first approach is intended to predict the final price trend in the coming days and the second approach aims to predict the final price values on the forecast horizon.
In the first approach, we use different classification models such as decision tree, random forest, logistic regression, SVM and MLP. Finally, using the ensemble model based on decision tree, random forest and SVM for the 3 data examined, the accuracy of the model has reached 74.1%, 71.75% and 70.24%. In the second approach, random forest regression model, support vector regression and LSTM deep neural networks are used and the models are compared for train, validation and test data. Test data with a forecast horizon of 5 days is considered. In these models, the LSTM algorithm in the test data has a MAPE error of less than 5% in all data. Finally, to combine the models and use both regression and classificayion models to predict the trend, we consider the stock historical data and test the hybrid models using the ensemble voting model. LSTM regression, random forest regression and support vector regression from regression models and On the other hand support vector machin and random forest from classification models are obtained as best combination to predict the next final price trend.
