Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example)
redicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Government of the Russian Federation, Financial University
2022-09-01
|
| Series: | Финансы: теория и практика |
| Subjects: | |
| Online Access: | https://financetp.fa.ru/jour/article/view/1729 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849303467040964608 |
|---|---|
| author | M. Kh. Abdalhammed A. M. Ghazal H. M. Ibrahim A. Kh. Ahmed |
| author_facet | M. Kh. Abdalhammed A. M. Ghazal H. M. Ibrahim A. Kh. Ahmed |
| author_sort | M. Kh. Abdalhammed |
| collection | DOAJ |
| description | redicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. The GARCH model is used to select appropriate historical periods for how the LSTM model works and to test proficiency at the weak, semi-strong, and strong levels. The data series obtained from the website (Investing.com) have been processed. The researchers have found that the performance of the neural network improves as the EPOCH value increases with a training (research) period of 50 days before, which is consistent with the results of the proficiency test at the weak level. It agrees with the results of the sufficiency test at the weak level, which indicates that in the case under study (the Bitcoin market is effective at the weak level). It is advised that crypto-currency investors rely more on the historical trend of the price of the currency than on its current price, taking advantage of the artificial neural network model (LSTM) in dealing with little data of high volatility. |
| format | Article |
| id | doaj-art-4e6dc2fd086f4292be24041873f6b9ec |
| institution | Kabale University |
| issn | 2587-5671 2587-7089 |
| language | Russian |
| publishDate | 2022-09-01 |
| publisher | Government of the Russian Federation, Financial University |
| record_format | Article |
| series | Финансы: теория и практика |
| spelling | doaj-art-4e6dc2fd086f4292be24041873f6b9ec2025-08-20T03:59:57ZrusGovernment of the Russian Federation, Financial UniversityФинансы: теория и практика2587-56712587-70892022-09-012649510810.26794/2587-5671-2022-26-4-95-108952Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example)M. Kh. Abdalhammed0A. M. Ghazal1H. M. Ibrahim2A. Kh. Ahmed3Tikrit University - College of Administration and EconomicsDamascus UniversityTikrit University - College of Administration and EconomicsTikrit University - College of Administration and Economicsredicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. The GARCH model is used to select appropriate historical periods for how the LSTM model works and to test proficiency at the weak, semi-strong, and strong levels. The data series obtained from the website (Investing.com) have been processed. The researchers have found that the performance of the neural network improves as the EPOCH value increases with a training (research) period of 50 days before, which is consistent with the results of the proficiency test at the weak level. It agrees with the results of the sufficiency test at the weak level, which indicates that in the case under study (the Bitcoin market is effective at the weak level). It is advised that crypto-currency investors rely more on the historical trend of the price of the currency than on its current price, taking advantage of the artificial neural network model (LSTM) in dealing with little data of high volatility.https://financetp.fa.ru/jour/article/view/1729cryptocurrencygarch modeldeep learningartificial neural networkslstm model |
| spellingShingle | M. Kh. Abdalhammed A. M. Ghazal H. M. Ibrahim A. Kh. Ahmed Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) Финансы: теория и практика cryptocurrency garch model deep learning artificial neural networks lstm model |
| title | Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) |
| title_full | Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) |
| title_fullStr | Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) |
| title_full_unstemmed | Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) |
| title_short | Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) |
| title_sort | application deep learning to predict crypto currency prices and their relationship to market adequacy applied research bitcoin as an example |
| topic | cryptocurrency garch model deep learning artificial neural networks lstm model |
| url | https://financetp.fa.ru/jour/article/view/1729 |
| work_keys_str_mv | AT mkhabdalhammed applicationdeeplearningtopredictcryptocurrencypricesandtheirrelationshiptomarketadequacyappliedresearchbitcoinasanexample AT amghazal applicationdeeplearningtopredictcryptocurrencypricesandtheirrelationshiptomarketadequacyappliedresearchbitcoinasanexample AT hmibrahim applicationdeeplearningtopredictcryptocurrencypricesandtheirrelationshiptomarketadequacyappliedresearchbitcoinasanexample AT akhahmed applicationdeeplearningtopredictcryptocurrencypricesandtheirrelationshiptomarketadequacyappliedresearchbitcoinasanexample |