درخت حوزه‌های تخصصی

Accounting earning represents the positive performance of companies during theirpertinent financial periods, thus it is assumed that accounting earning will benoted by investors, which could contribute to the optimum allocation of resourcesto successful companies. It can also play a major role in the economic growth anddevelopment of a society. This research focuses in the relationship between accountingearning and gross domestic product (GPD). The research populationincludes all companies listed in Tehran Stock Exchange. Due to the limitations ofsample size, 65 companies between 2009 and 2013 were studied. The findingsshowed that there is a significant relationship between accounting earning andGPD growth rate. A similar relationship was also observed between accountingearning and forecast errors of GPD growth. The control variables including inflationand employment rate had a significant relationship with GPD growth rate andforecast errors of GPD growth.

The secret to achieving your organization's goals in complex and challenging environments is to make the right managerial and rational decisions. In this regard, the accounting information system as one of the sources of information for the decision of managers is of particular importance. Therefore, in order to achieve these goals, it is necessary to have an accounting information system with dynamic capabilities. The dynamic capability of the accounting information system (hidden variable) was measured by the observed variables of flexibility, continuous evaluation, continuous investment and system variability. Therefore, based on this argument, the aim of the present study is to provide a dynamic capability model of the accounting system based on the financial effectiveness of the management accounting system. Data envelopment analysis is a well-known methodology that is applied to evaluate the selected firms based on the most important features. The results of analysis of the proposed method on 86 companies listed on the Tehran Stock Exchange and analysis and analysis of data by structural equation modeling show that the dynamic capability of the accounting information system consists of (flexibility, continuous evaluation, continuous investment and system variability). The result indicate that the management accounting system is effective.

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.