AI-ENHANCED INSIGHTS INTO YEMEN RIYAL'S EXCHANGE RATE: UNRAVELING LONG-TERM BEHAVIOR THROUGH MARKOV CHAIN ANALYSIS | ||||
| Journal of Al-Azhar University Engineering Sector | ||||
| Volume 19, Issue 71, April 2024, Page 732-747 PDF (652.28 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/auej.2024.262498.1588 | ||||
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| Authors | ||||
Ghassan Ahmed Ali  1; Shehab Abdulhabib Saeed Alzaeemi2; Haji Abdul Hafidz bin Haji Omar1; Dayang Hajah Tiawa binti Awang Haji Hamid1; Abdul Azeem Khan1
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| 1Faculty of Islamic Technology, Universiti Islam Sultan Sharif Ali, Brunei Darussalam | ||||
| 2Mathematical Department, Sana’a Community College, Sana’a, Yemen | ||||
| Abstract | ||||
| The study of foreign exchange (FOREX) markets is known as foreign currency exchange. The FOREX market is where exchange rates are determined and traded. The prices of one currency, expressed in terms of another money, are defined as exchange rates. Exchange rates provide crucial data for international monetary exchange markets. Upon using the Markov chain model and R program, this study aims to determine the behaviour of the Yemeni Ryle’s currency rate against the US dollar (USD) with three states observed in the study. The three different movements are three other states based on the Markov chain to develop the transition probability matrix. The results showed that the exchange rate of the Yemeni Ryle could be categorized into one of three states at the end of each day during the study period. The transition probability matrix and starting state vector were calculated. The results also showed the probability of being in one of these three states, namely ‘increases,’ ‘remains the same,’ or ‘decreases,’ which signify 0.3614, 0.3268, and 0.3118, respectively. Moreover, the expected number of visits and return time were obtained. This result showed that the chain will visit the state of ‘de-creases’ (D) in three days on average. This study has shown how the utilized Markov model can fit data with the ability to predict the trend. Therefore, this model can help researchers and investors determine and make informed business decisions in a foreign exchange market, influenced by various market factors, including market forces and psychological factors affecting investors. | ||||
| Keywords | ||||
| Markov chain, Artificial Intelligence, Foreign Exchange, Exchange rates of the Yemeni Riyal, R Program; Ex-pected Number of Visits | ||||
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