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Journals / Journal of Soft Computing and Artificial Intelligence / 2021 / Cilt: 2 - Sayı: 2

Forecasting Turkish Lira (TRY)/US Dollar (USD) Interest Exchange Rates Using Machine Learning Methodologies

Mahmut DİRİK
Pages
120–131
Publication Date
2021-12-15
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These metric results show that the proposed model is suitable for both prediction and investment data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Machine learning algorithms have become increasingly popular in recent years for analyzing financial data and predicting the exchange rate system.
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Key Findings & Data:The aim of this paper was to construct an investment appreciation rate estimation model based on machine learning by estimating the Turkish lira/US dollar exchange rate.
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Conclusion & Impact:These metric results show that the proposed model is suitable for both prediction and investment data.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine Learning · Exchange Rate Prediction · Regression

Abstract

Machine learning algorithms have become increasingly popular in recent years for analyzing financial data and predicting the exchange rate system. The aim of this paper was to construct an investment appreciation rate estimation model based on machine learning by estimating the Turkish lira/US dollar exchange rate. The forecasting model was developed using foreign exchange market data, namely the exchange rates in TL and USD at specific periods. The proposed model was estimated using machine learning methods such as Multilayer Perceptron (MLP), Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Local Weighted Learning (LWL). The model's validity was established using TRY interest rates and the USD exchange rate. The data were analyzed using mean absolute error (MAE), directional accuracy (DA), mean square error (MSE), and root mean square error (RMSE). These metric results show that the proposed model is suitable for both prediction and investment data.

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Cite This Article

DİRİK, M. (2021). Forecasting Turkish Lira (TRY)/US Dollar (USD) Interest Exchange Rates Using Machine Learning Methodologies. Journal of Soft Computing and Artificial Intelligence, Cilt: 2 - Sayı: 2, 120–131. https://ajindex.com/articles/779575

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References (2)

  1. M. Štěpnička, P. Cortez, J. P. Donate, and L. Štěpničková, “Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations,” Expert Syst. Appl., vol. 40, no. 6, pp. 1981–1992, 2013, doi: 10.1016/j.eswa.2012.10.001. M. C. Lee, “Using support vector machine with a hybrid feature selection method to the stock trend prediction,” Expert Syst. Appl., vol. 36, no. 8, pp. 10896–10904, 2009, doi: 10.1016/j.eswa.2009.02.038. M. Yasir et al., “An intelligent event-sentiment-based daily foreign exchange rate forecasting system,” Appl. Sci., vol. 9, no. 15, 2019, doi: 10.3390/app9152980. D. Shah, H. Isah, and F. Zulkernine, “Stock market analysis: A review and taxonomy of prediction techniques,” Int. J. Financ. Stud., vol. 7, no. 2, 2019, doi: 10.3390/ijfs7020026. M. J. S. de Souza, D. G. F. Ramos, M. G. Pena, V. A. Sobreiro, and H. Kimura, “Examination of the profitability of technical analysis based on moving average strategies in BRICS,” Financ. Innov., vol. 4, no. 1, 2018, doi: 10.1186/s40854-018-0087-z. F. Cavalli, A. Naimzada, and M. Pireddu, “An evolutive financial market model with animal spirits: imitation and endogenous beliefs,” J. Evol. Econ., vol. 27, no. 5, pp. 1007–1040, 2017, doi: 10.1007/s00191-017-0506-8.
  2. J. Contreras, R. Espínola, F. J. Nogales, and A. J. Conejo, “ARIMA models to predict next-day electricity prices,” IEEE Trans. Power Syst., vol. 18, no. 3, pp. 1014–1020, 2003, doi: 10.1109/TPWRS.2002.804943. T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” J. Econ., vol. 31, pp. 307–327, 1986, doi: 10.3905/jpm.2019.1.098. M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Syst. Appl., vol. 37, no. 1, pp. 479–489, 2010, doi: 10.1016/j.eswa.2009.05.044. S. Galeshchuk, “Neural networks performance in exchange rate prediction,” Neurocomputing, vol. 172, pp. 446–452, 2016, doi: 10.1016/j.neucom.2015.03.100. B. Amrouche and X. Le Pivert, “Artificial neural network based daily local forecasting for global solar radiation,” Appl. Energy, vol. 130, no. 2014, pp. 333–341, 2014, doi: 10.1016/j.apenergy.2014.05.055. S. Agatonovic-Kustrin and R. Beresford, “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research,” J. Pharm. Biomed. Anal., vol. 22, no. 5, pp. 717–727, 2000, doi: 10.1016/S0731-7085(99)00272-1. “Investing in Turkey, 2010-2021 | Kaggle.” https://www.kaggle.c