hamed mirashk; Amir Albadvi; mehrdad kargari; Mohammadali Rastegar Sorkhe; mohammad talebi
Volume 27, Issue 4 , January 2024, , Pages 138-168
Abstract
The Design Science Research method (DSR) is an approach to provide practical solutions based on scientific principles in order to produce substantiated and inferred results and products, and at the same time, the results can be scientifically evaluated in the form of primary artifacts and practical use ...
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The Design Science Research method (DSR) is an approach to provide practical solutions based on scientific principles in order to produce substantiated and inferred results and products, and at the same time, the results can be scientifically evaluated in the form of primary artifacts and practical use in four main stages which ultimately results in their practical efficiency and effectiveness in the outside world. By designing and creating an archetype in the prototyping stage, DSR evaluates real scenarios and then examines the solution in practical cases. From this point of view, in this research, it has been tried to use the DSR method to provide an innovative solution for predicting bank liquidity risk and upcoming scenarios. This study uses semtiment analysis and deep learning algorithm such as deep convolutional network in predicting liquidity risk and presents a simple and effective method to identify dynamic qualitative variables from recent news about a domestic bank in the country. Predicted scenarios are available to banking experts in the real world to facilitate decision-making in risk measures. According to the guidelines of the Basel Committee and other European banking regulatory frameworks, comparing these scenarios with the scenarios occurring in the bank indicates a relatively high accuracy of the proposed method. In the scenarios derived from the Basel Committee and derived from the European Banking Authority, the forecasting accuracy is about 91% and 82%, respectively
Parisa Zolfaghar; ameneh khadivar; fatemeh abbasi
Volume 26, Issue 2 , July 2022, , Pages 18-41
Abstract
Abstract Due to the global attention to cryptocurrencies especially bitcoin, governments, banks, and other industries are expected to use cryptocurrencies in their daily transactions. Therefore, as any other financial field, there is a need to identify the challenges in this field to safe ...
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Abstract Due to the global attention to cryptocurrencies especially bitcoin, governments, banks, and other industries are expected to use cryptocurrencies in their daily transactions. Therefore, as any other financial field, there is a need to identify the challenges in this field to safe investment. On the other hand, by the expansion of social networks, unstructured data is increasing, which can be used to create added value in various areas such as sentiment analysis. Therefore, this study was conducted to investigate the impact of perceived risk by social network users on the price of Bitcoin. According to the research findings, the identified risks in the field of bitcoin include social, economic, security, technological and legal risks., The conversations on the Bitcoin Talk Site were used to extract the bitcoin risks. After collecting these conversations by the web crawler, the conversations were clustered into thematic clusters using the Latent Dirichlet Allocation algorithm, which is one of the most popular methods in Topic Modeling. were analyzed using vocabulary-based method and AFINN dictionary. NARX Neural Network was used to measure the effect of Userschr('39') Sentiment on the price of Bitcoin. The results show a correlation of 0.99 and a mean square error of 0.001, which means that there is a correlation between the actual price and the predicted price of Bitcoin. The findings of this study can attract the attention of financial actors and businessmen in the field of bitcoin to plan a safe investment and reduce risk.
