Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example)
https://doi.org/10.26794/2587-5671-2022-26-4-95-108
Abstract
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.
About the Authors
M. Kh. Abdalhammed
Tikrit University - College of Administration and Economics
Iraq
Competing Interests:
Iraq
Moudher Kh. Abdalhammed - Ph.D., Prof., Department of Management and Economics
Tikrit
Competing Interests:
The authors have no conflicts of interest to declare
A. M. Ghazal
Damascus University
Syrian Arab Republic
Competing Interests:
Syrian Arab Republic
Ahmad M. Ghazal - Assistant Prof., Faculty of Economics, Department of banking and
insurance
Damascus
Competing Interests:
The authors have no conflicts of interest to declare
H. M. Ibrahim
Tikrit University - College of Administration and Economics
Iraq
Competing Interests:
Iraq
Hanan M. Ibrahim - Master’s in Business Administration/Financial Management, Faculty of Business Administration
Tikrit
Competing Interests:
The authors have no conflicts of interest to declare
A. Kh. Ahmed
Tikrit University - College of Administration and Economics
Iraq
Competing Interests:
Iraq
Ahmed Kh. Ahmed - Assistant Prof., Master’s in Business Administration/Financial Management, Faculty of Management and Economics, Department of Public Administration/Faculty of Business Administration
Tikrit
Competing Interests:
The authors have no conflicts of interest to declare
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Review
For citations:
Abdalhammed M.Kh., Ghazal A.M., Ibrahim H.M., Ahmed A.Kh. Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example). Finance: Theory and Practice. 2022;26(4):95-108. https://doi.org/10.26794/2587-5671-2022-26-4-95-108
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