جستجوی مقالات مرتبط با کلیدواژه « انتخاب ویژگی » در نشریات گروه « مالی »

Because of the numerous environmental, industrial, micro, and macro elements that influence the index and stock price trend, it is undeniably difficult to predict changes in the index and stock price trend. Although the aforementioned factors are difficult or impossible to measure in some cases, micro factors such as price history and trade volume are simply measurable and available. The goal of this research is to use feature selection and classification algorithms to find the most effective features and indicators for predicting the total index and total weighted index. Then we will examine the proposed model in the daily and weekly time period from January 2020 to May 2022. The results show that it is possible to predict the trend of changes with high accuracy using a limited number of indicators, and that the prediction accuracy is much higher in the weekly time interval than in the daily time interval.

The stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features create a wide state space. Hence, it is impractical to provide a data set containing all these combinations to train the stock index prediction model. in this research, an attempt has been made, after collecting a significant number of effective features on the index, to provide a method for selecting appropriate features for the stock index prediction model with aim of increasing prediction accuracy. For this purpose, the mRMR algorithm is used as the basic algorithm. Also, to select the appropriate model, a number of the most applicable artificial intelligence models for predicting the stock index were compared and according to the results, the LSTM network was selected to predict the stock index. The results of this study show that using the LSTM network and the proposed method in selecting features, with 8 selected features, high accuracy can be achieved in the daily prediction of the Tehran Stock Exchange Index. So that MPE is calculated to be about 2.66,

Stock markets play a significant role in organizing modern economic systems. Several research projects have been performed in the field of prediction using intelligent techniques. Considering that the accuracy of these techniques is significantly affected by their input features, one of the improvements made in the use of intelligent models, in addition to the application of hybrid models, is the use of dimensionality reduction as a preprocessing for the prediction model. In this study, in order to predict the daily index of the Tehran Stock Exchange, two methods of dimensional reduction (selection and extraction) are used simultaneously to select appropriate features as model inputs. Hence, the MRMR-MID algorithm is used to select features and the PCA algorithm is used to extract features. Then, support vector regression is used as a prediction model. Finally, an algorithm for selecting suitable features is proposed as ISF_ MID, according to the results obtained from the analysis of the use of dimensional reduction techniques in the prediction model. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange. It should be noted that the studied models were evaluated in the implementation stage by the k-fold cross-validation method. MAE, MSE, and RMSE criteria are also used to evaluate the performance of these models.

One of the most important issues faced by banks and financial institutions is the issue of credit risk. The significant amount of deferred bank claims around the world indicates the importance of this issue and the need to pay attention to it. So far, many efforts have been made to provide an effective model for evaluating and classification credit applicants as accurately as possible. In this regard, the present study attempts to provide a new approach for assessing the credit risk of bank customers. The support vector machine(SVM) method is combined as the main classifier of banking customers, with a feature selection method called the Binary Ant Colony Optimization Algorithm(BACO-SVM). In order to demonstrate the effectiveness of the proposed method, we used data from 85 companies from legal recipients of facilities of an Iranian bank in a 5 year interval (1393-1893) along with 16 characteristics related to each of them. The results of the BACO-SVM method have been compared with the PSO-SVM, GA-SVM, and SVM method. The results of the research indicated that BACO-SVM model has better performance in assessing credit risk rather than other methods. As the result, using the BACO-SVM method, we classify customers into two groups of good and bad account customers. Finally, in order to increase the flexibility in decision making, we will rank our good account customers with the VIKOR method. This rating will lead to a more accurate assessment of the credit risk situation of good account applicants.