Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction

Furkan Türkoğlu; Eda Göçecek; Yavuz Yumrukuz

doi:10.46520/bddkdergisi.1600294

Research Article

Makine Öğrenmesi ve Derin Öğrenme Yaklaşımlarının Döviz Kuru Tahmini Konusundaki Tahmin Yeteneği

Year 2024, , 186 - 210, 12.12.2024

Furkan Türkoğlu Eda Göçecek Yavuz Yumrukuz

https://doi.org/10.46520/bddkdergisi.1600294

Abstract

References

1. Fama, E. F. (1984). Forward and spot exchange rates. Journal of Monetary Economics, 14(3), 319-338.
2. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., 2008. Fast unfolding of communities in large networks. J. Stat. Mech.Theory Exp. 2008, P10008.
3. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
4. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
5. Kaushik, M., & Giri, A. K. (2020). Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques.
6. Goncu, A. (2019). Prediction of Exchange Rates with Machine Learning.
7. Ajumi, O., & Kaushik, A. (2018). Exchange Rates Prediction via Deep Learning and Machine Learning: A Literature Survey on Currency Forecasting.
8. Dautel, A. J., Härdle, W. K., Lessmann, S., & Seow, H.-V. (2020). Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks.
9. Kim, W. J., Jung, G., & Choi, S.-Y. (2020). Forecasting CDS Term Structure Based on NelsonSiegel Model and Machine Learning. Complexity, 2020.
10. Chen, Q., Tsang, E., 2013. Forecasting macroeconomic and financial time series. Physica A, 392, 529–537.
11. Chen, Q., Wu, T.T., Fang, M., 2013. Detectinglocalcommunitystructure in complex networks based on local degree central nodes. Physica A. 392, 529–537.
12. Chen, T., Guestrin, C., 2016, XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
13. Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y., 2014. On the properties of neural machine translation: Encoder-decoder approaches. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, 103-111.
14. Clauset, A., Newman, M.E.J., Moore, C., 2004. Finding community structure in very large networks. Phys. Rev. E. 70, 066111.
15. Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A., 2005. Comparing community structure identification. J. Stat. Mech.-Theory Exp. , P09008.
16. Deboeck, G., 1994. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets. New York: John Wiley & Sons.
17. Fabio, D.R., Fabio, D., Carlo, P., 2013. Profiling core-periphery network structure by random walkers. Sci. Rep. 3, 1467.
18. Fabricio, B., Liang, Z., 2013. Fuzzy community structure detection by particle competition and cooperation. Soft Comput. 17, 659–673.
19. Fortunato, S., 2010. Community detection in graphs. Phys. Rep.-Rev. Sec. Phys. Lett. 486, 75–174.
20. Fortunato, S., Barthelemy, M., 2007. Resolution limit in community detection. Proc. Natl. Acad. Sci. U. S. A. 104, 36–41.
21. Gregory, S., 2011. Fuzzy overlapping communities in networks. J. Stat. Mech.-Theory Exp. , P02017.
22. Havens, T.C., Bezdek, J.C., Leckie, C., R.K., Palaniswami, M., 2013. A soft modularity function for detecting fuzzy communities in social networks. IEEE Trans. Fuzzy Syst. 21, 1170–1175.
23. Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
24. Hullermeier, E., Rifqi, M., 2009. A fuzzy variant of the rand index for comparing clustering structures, in: in Proc. IFSA/EUSFLAT Conf., pp. 1294–1298.
25. Karush, W., 1939. Minima of functions of several variables with inequalities as side constraints. Master's Thesis, Department of Mathematics, University of Chicago.
26. Kamruzzaman, J., 2003. Multi-Layer Perceptron (MLP) for Time Series Prediction: A Review. Australian Journal of Intelligent Information Processing Systems, 8(2), 52-58.
27. Kuhn, H. W., Tucker, A. W., 1951. Nonlinear programming. Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, 481-492.
28. Lancichinetti, A., Fortunato, S., 2009. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E. 80, 016118.
29. Lancichinetti, A., Fortunato, S., Radicchi, F., 2008. Benchmark graphs for testing community detection algorithms. Phys. Rev. E. 78, 046110.
30. Li, J., Wang, X., Eustace, J., 2013. Detecting overlapping communities by seed community in weighted complex networks. Physica A. 392, 6125–6134.
31. Liu, J., 2010. Fuzzy modularity and fuzzy community structure in networks. Eur. Phys. J. B. 77, 547–557.
32. Liu, W., Pellegrini, M., Wang, X., 2014. Detecting communities based on network topology. Sci. Rep. 4, 5739.
33. Lou, H., Li, S., Zhao, Y., 2013. Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Physica A. 392, 3095–3105.
34. Nepusz, T., Petróczi, A., Négyessy, L., Bazsó, F., 2008. Fuzzy communities and the concept of bridgeness in complex networks. Phys. Rev. E. 77, 016107.
35. Newman, M.E.J., Girvan, M., 2004. Finding and evaluating community structure in networks. Phys. Rev. E. 69, 026113.
36. Nyoni, Thabani., 2018. Univariate time series analysis and forecasting models in economics. Journal of Economics and Finance, 1(1), 1-15.
37. Psorakis, I., Roberts, S., Ebden, M., Sheldon, B., 2011. Overlapping community detection using bayesian non-negative matrix factorization. Phys. Rev. E. 83, 066114.
38. Raghavan, U., Albert, R., Kumara, S., 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev E. 76, 036106.
39. Sobolevsky, S., Campari, R., 2014. General optimization technique for high-quality community detection in complex networks. Phys. Rev. E. 90, 012811.
40. Sun, P., Gao, L., Han, S., 2011. Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks. Inf. Sci. 181, 1060–1071.
41. Vehlow, C., Reinhardt, T., Weiskopf, D., 2013. Visualizing fuzzy overlapping communities in networks. IEEE Trans. Vis. Comput. Graph. 19, 2486–2495.
42. Šubelj, L., Bajec, M., 2011a. Robust network community detection using balanced propagation. Eur. Phys. J. B. 81, 353–362.
43. Šubelj, L., Bajec, M., 2011b. Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction. Phys. Rev. E. 83, 036103.
44. Šubelj, L., Bajec, M., 2012. Ubiquitousness of link-density and linkpattern communities in real-world networks. Eur. Phys. J. B. 85, 1–11.
45. Vapnik, V., 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
46. Wang, W., Liu, D., Liu, X., Pan, L., 2013. Fuzzy overlapping community detection based on local random walk and multidimensional scaling. Physica A. 392, 6578–6586.
47. Wang, X., Li, J., 2013. Detecting communities by the core-vertex and intimate degree in complex networks. Physica A. 392, 2555–2563.
48. Zhang, S., Wang, R., Zhang, X., 2007. Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A. 374, 483–490.
49. Zhang, Y., Yeung, D., 2012. Overlapping community detection via bounded nonnegative matrix tri-factorization, in: In Proc. ACM SIGKDD Conf., pp. 606–614.

Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction

Year 2024, , 186 - 210, 12.12.2024

Furkan Türkoğlu Eda Göçecek Yavuz Yumrukuz

https://doi.org/10.46520/bddkdergisi.1600294

Abstract

This study evaluates the efficacy of forecasting models in predicting USD/TRY exchange rate
fluctuations. We assess Support Vector Machine (SVM), XGBoost, Long Short-Term Memory (LSTM), and
Gated Recurrent Unit (GRU) models with 96 and 21 feature sets. Data from 01.01.2010 to 30.04.2024 were
sourced from Bloomberg, CBRT, and BDDK. Findings indicate that LSTM and GRU models outperform
traditional models, with GRU showing the highest predictive accuracy. SVM performs poorly with highdimensional data,
while XGBoost offers moderate predictive power but lacks in capturing intricate
patterns. This study highlights the importance of model and feature selection in financial time series
forecasting and underscores the advantages of advanced neural networks. The results provide valuable
insights for analysts and policymakers in developing robust economic forecasting models.

Keywords

Exchange rate, Machine learning, Deep learning, Time series forecasting, Nelson Siegel model, Yield curve

References

1. Fama, E. F. (1984). Forward and spot exchange rates. Journal of Monetary Economics, 14(3), 319-338.
2. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., 2008. Fast unfolding of communities in large networks. J. Stat. Mech.Theory Exp. 2008, P10008.
3. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
4. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
5. Kaushik, M., & Giri, A. K. (2020). Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques.
6. Goncu, A. (2019). Prediction of Exchange Rates with Machine Learning.
7. Ajumi, O., & Kaushik, A. (2018). Exchange Rates Prediction via Deep Learning and Machine Learning: A Literature Survey on Currency Forecasting.
8. Dautel, A. J., Härdle, W. K., Lessmann, S., & Seow, H.-V. (2020). Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks.
9. Kim, W. J., Jung, G., & Choi, S.-Y. (2020). Forecasting CDS Term Structure Based on NelsonSiegel Model and Machine Learning. Complexity, 2020.
10. Chen, Q., Tsang, E., 2013. Forecasting macroeconomic and financial time series. Physica A, 392, 529–537.
11. Chen, Q., Wu, T.T., Fang, M., 2013. Detectinglocalcommunitystructure in complex networks based on local degree central nodes. Physica A. 392, 529–537.
12. Chen, T., Guestrin, C., 2016, XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
13. Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y., 2014. On the properties of neural machine translation: Encoder-decoder approaches. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, 103-111.
14. Clauset, A., Newman, M.E.J., Moore, C., 2004. Finding community structure in very large networks. Phys. Rev. E. 70, 066111.
15. Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A., 2005. Comparing community structure identification. J. Stat. Mech.-Theory Exp. , P09008.
16. Deboeck, G., 1994. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets. New York: John Wiley & Sons.
17. Fabio, D.R., Fabio, D., Carlo, P., 2013. Profiling core-periphery network structure by random walkers. Sci. Rep. 3, 1467.
18. Fabricio, B., Liang, Z., 2013. Fuzzy community structure detection by particle competition and cooperation. Soft Comput. 17, 659–673.
19. Fortunato, S., 2010. Community detection in graphs. Phys. Rep.-Rev. Sec. Phys. Lett. 486, 75–174.
20. Fortunato, S., Barthelemy, M., 2007. Resolution limit in community detection. Proc. Natl. Acad. Sci. U. S. A. 104, 36–41.
21. Gregory, S., 2011. Fuzzy overlapping communities in networks. J. Stat. Mech.-Theory Exp. , P02017.
22. Havens, T.C., Bezdek, J.C., Leckie, C., R.K., Palaniswami, M., 2013. A soft modularity function for detecting fuzzy communities in social networks. IEEE Trans. Fuzzy Syst. 21, 1170–1175.
23. Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
24. Hullermeier, E., Rifqi, M., 2009. A fuzzy variant of the rand index for comparing clustering structures, in: in Proc. IFSA/EUSFLAT Conf., pp. 1294–1298.
25. Karush, W., 1939. Minima of functions of several variables with inequalities as side constraints. Master's Thesis, Department of Mathematics, University of Chicago.
26. Kamruzzaman, J., 2003. Multi-Layer Perceptron (MLP) for Time Series Prediction: A Review. Australian Journal of Intelligent Information Processing Systems, 8(2), 52-58.
27. Kuhn, H. W., Tucker, A. W., 1951. Nonlinear programming. Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, 481-492.
28. Lancichinetti, A., Fortunato, S., 2009. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E. 80, 016118.
29. Lancichinetti, A., Fortunato, S., Radicchi, F., 2008. Benchmark graphs for testing community detection algorithms. Phys. Rev. E. 78, 046110.
30. Li, J., Wang, X., Eustace, J., 2013. Detecting overlapping communities by seed community in weighted complex networks. Physica A. 392, 6125–6134.
31. Liu, J., 2010. Fuzzy modularity and fuzzy community structure in networks. Eur. Phys. J. B. 77, 547–557.
32. Liu, W., Pellegrini, M., Wang, X., 2014. Detecting communities based on network topology. Sci. Rep. 4, 5739.
33. Lou, H., Li, S., Zhao, Y., 2013. Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Physica A. 392, 3095–3105.
34. Nepusz, T., Petróczi, A., Négyessy, L., Bazsó, F., 2008. Fuzzy communities and the concept of bridgeness in complex networks. Phys. Rev. E. 77, 016107.
35. Newman, M.E.J., Girvan, M., 2004. Finding and evaluating community structure in networks. Phys. Rev. E. 69, 026113.
36. Nyoni, Thabani., 2018. Univariate time series analysis and forecasting models in economics. Journal of Economics and Finance, 1(1), 1-15.
37. Psorakis, I., Roberts, S., Ebden, M., Sheldon, B., 2011. Overlapping community detection using bayesian non-negative matrix factorization. Phys. Rev. E. 83, 066114.
38. Raghavan, U., Albert, R., Kumara, S., 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev E. 76, 036106.
39. Sobolevsky, S., Campari, R., 2014. General optimization technique for high-quality community detection in complex networks. Phys. Rev. E. 90, 012811.
40. Sun, P., Gao, L., Han, S., 2011. Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks. Inf. Sci. 181, 1060–1071.
41. Vehlow, C., Reinhardt, T., Weiskopf, D., 2013. Visualizing fuzzy overlapping communities in networks. IEEE Trans. Vis. Comput. Graph. 19, 2486–2495.
42. Šubelj, L., Bajec, M., 2011a. Robust network community detection using balanced propagation. Eur. Phys. J. B. 81, 353–362.
43. Šubelj, L., Bajec, M., 2011b. Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction. Phys. Rev. E. 83, 036103.
44. Šubelj, L., Bajec, M., 2012. Ubiquitousness of link-density and linkpattern communities in real-world networks. Eur. Phys. J. B. 85, 1–11.
45. Vapnik, V., 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
46. Wang, W., Liu, D., Liu, X., Pan, L., 2013. Fuzzy overlapping community detection based on local random walk and multidimensional scaling. Physica A. 392, 6578–6586.
47. Wang, X., Li, J., 2013. Detecting communities by the core-vertex and intimate degree in complex networks. Physica A. 392, 2555–2563.
48. Zhang, S., Wang, R., Zhang, X., 2007. Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A. 374, 483–490.
49. Zhang, Y., Yeung, D., 2012. Overlapping community detection via bounded nonnegative matrix tri-factorization, in: In Proc. ACM SIGKDD Conf., pp. 606–614.

There are 49 citations in total.

Details

Primary Language	English
Subjects	Financial Institutions, Banking and Insurance (Other)
Journal Section	Research Articles
Authors	Furkan Türkoğlu Eda Göçecek Yavuz Yumrukuz
Publication Date	December 12, 2024
Submission Date	July 25, 2024
Acceptance Date	September 11, 2024
Published in Issue	Year 2024

Cite

APA	Türkoğlu, F., Göçecek, E., & Yumrukuz, Y. (2024). Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction. BDDK Bankacılık Ve Finansal Piyasalar Dergisi, 18(2), 186-210. https://doi.org/10.46520/bddkdergisi.1600294

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