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Predicting Financial Stress Index Using Wavelet Transform Artificial Neural Networks

Year 2022, Volume: 7 Issue: 3, 282 - 296, 07.10.2022
https://doi.org/10.23834/isrjournal.1159770

Abstract

Considering the problems encountered due to financial risks and uncertainties, it is of great importance to determine the financial stress index. The aim of the study is to predict the level of financial stress index by using machine learning methods. For this purpose, the weekly time series of the financial stress index were examined using independent and hybrid models. While artificial neural networks are used as stand-alone machine learning models, wavelet transform is used as a preprocessing technique to create hybrid Models. In addition, lag lengths were obtained by using autocorrelation functions to increase model accuracy in financial stress index predictions. The findings of the study were evaluated in terms of various performance indicators. It has been determined that the wavelet transform artificial neural network model performs better than the pure artificial neural network model in predicting the financial stress index. It is thought that the results of the study will be beneficial for researchers and practitioners following the financial stress index.

References

  • Aghelpour, P., Mohammadi, B., & Biazar, S. M. (2019). Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoretical and Applied Climatology, 138(3), 1471-1480.
  • Akay, E. Ç., Topal, K. H., Kizilarslan, S. & Bulbul, H. (2019). Forecasting of Turkish housing price index: ARIMA, random forest, ARIMA-random forest. Pressacademia, 10(10), 7-11.
  • Aker, Y. (2012). Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model. Avrupa Bilim ve Teknoloji Dergisi, (35), 89-93.
  • Aksoy, B. (2021). Pay senedi fiyat yönünün makine öğrenmesi yöntemleri ile tahmini: Borsa İstanbul örneği. Business and Economics Research Journal, 12(1), 89-110.
  • Alsu, E. (2020). Finansal Stres İndeksi İle Doğrudan Yabancı Yatırımlar, Portföy Yatırımları Ve Dış Borç Stoku Arasındaki İlişki: ARDL Sınır Testi. ETÜ Sentez İktisadi ve İdari Bilimler Dergisi, (1), 27-40.
  • Arslankaya, S. & Toprak, Ş. (2021). Makine Öğrenmesi ve Derin Öğrenme Algoritmalarını Kullanarak Hisse Senedi Fiyat Tahmini. International Journal of Engineering Research and Development, 13(1), 178-192.
  • Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F. & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119.
  • Aygören, H., Saritaş, H. & Morali, T. (2012). İMKB 100 endeksinin yapay sinir ağları ve newton nümerik arama modelleri ile tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 4(1), 73-88.
  • Balakrishnan, R., Dysainger, S., Tytell, I. & Elekdag, S. A. (2009). The transmission of financial stress from advanced to emerging economies.
  • Balakrishnan, R., Dysainger, S., Elekdag, S. & Tytell, I. (2011). The transmission of financial stress from advanced to emerging economies. Emerging Markets Finance and Trade, 47(sup2), 40-68.
  • Başakın, E. E., Ekmekcioğlu, Ö., Çıtakoğlu, H. & Özger, M. (2022). A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Computing and Applications, 34(1), 783-812.
  • Bueno, S., & Salmeron, J. L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert systems with Applications, 36(3), 5221-5229.
  • Bülbül, H. & Akgül, I. (2018). Türkiye Finansal Stres Endeksi ve Markov Rejim Değişim Modeli ile Yüksek Stres Dönemlerinin Belirlenmesi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 13(3), 125-140.
  • Cardarelli, R., Elekdag, S. & Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, 7(2), 78-97.
  • Çamlica, F. & Gunes, D. (2016). Turkiye'de Finansal Stresin olculmesi: Yontemsel Bir Karsilastirma (No. 1606). Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
  • Dingli, A. & Fournier, K. S. (2017). Financial time series forecasting-a deep learning approach. International Journal of Machine Learning and Computing, 7(5), 118-122.
  • Ebadati, O. M. E. & Mortazavi, M. T. (2018). An efficient hybrid machine learning method for time series stock market forecasting. Neural Network World, 28(1), 41-55.
  • FRED, Federal Reserve Bank of St. Louis, St. Louis Fed Financial Stress Index (DISCONTINUED) [STLFSI2], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/STLFSI2, July, 2022.
  • Ghazanfar, M. A., Alahmari, S. A., Aldhafiri, Y. F., Mustaqeem, A., Maqsood, M., & Azam, M. A. (2017). Using machine learning classifiers to predict stock exchange index. International Journal of Machine Learning and Computing, 7(2), 24-29.
  • Hakkio, C. S. & Keeton, W. R. (2009). Financial stress: What is it, how can it be measured, and why does it matter. Economic Review, 94(2), 5-50.
  • Hoque, M. E. & Low, S. W. (2022). Reactions of Bitcoin and Gold to Categorical Financial Stress: New Evidence from Quantile Estimation. Risks, 10(7), 136.
  • Karacan, S. & Kırdar, M. (2021). Hisse Senedi Fiyat Tahmininde Makine Öğrenmesi ve Yapay Zeka Kullanımı. Journal of International Social Research, 14(76).
  • Kartal, C. (2020). Destek Vektör Makineleri ile Borsa Endekslerinin Tahmini. Itobiad: Journal of The Human & Social Science Researches, 9(2).
  • Kaynar, T., & Yiğit, Ö. E. (2021). Öznitelik Mühendisliği ile Makine Öğrenmesi Yöntemleri Kullanılarak BIST 100 Endeksi Değişiminin Tahminine Yönelik Bir Yaklaşım. Yaşar Üniversitesi E-Dergisi, 16(64), 1741-1762.
  • Duprey, T. & Klaus, B. (2017). How to Predict Financial Stress? An Assessment of Markov Switching Models (No. 2017-32). Bank of Canada.
  • Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. Journal of Financial Stability, 2(3), 243-265.
  • Luo, J., Chen, H., Xu, Y., Huang, H. & Zhao, X. (2018). An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling, 64, 654-668.
  • Monin, P. J. (2019). The OFR financial stress index. Risks, 7(1), 25.
  • Pabuçcu, H. (2019). Borsa Endeksi Hareketlerinin Makine Öğrenme Algoritmalari İle Tahmini. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 179-190.
  • Panchal, G., Ganatra, A., Kosta, Y. P. & Panchal, D. (2011). Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering, 3(2), 332-337.
  • Parray, I. R., Khurana, S. S., Kumar, M. & Altalbe, A. A. (2020). Time series data analysis of stock price movement using machine learning techniques. Soft Computing, 24(21), 16509-16517.
  • Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172.
  • Sandahl, J. F., Holmfeldt, M., Rydén, A. & Strömqvist, M. (2011). An index of financial stress for Sweden. S v ER ig ESR ik S bank, 2. Slingenberg, J. W. & De Haan, J. (2011). Forecasting financial stress. DNB Working Paper, No. 292/April.
  • Vdovychenko, A. & Oros, G. (2015). Financial stress index: estimation and application in empirical researches in Ukraine. Financial and Banking Services Market, Vol. 14 (No2).
  • Yiğiter, Ş. Y., Sarı, S. S. & Başakın, E. E. (2017). Hisse senedi kapanış fiyatlarının yapay sinir ağları ve bulanık mantık çıkarım sistemleri ile tahmin edilmesi.

Finansal Stres Endeksinin Dalgacık Dönüşümlü Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi

Year 2022, Volume: 7 Issue: 3, 282 - 296, 07.10.2022
https://doi.org/10.23834/isrjournal.1159770

Abstract

Finansal risk ve belirsizlikler nedeniyle karşılaşılan problemler dikkate alındığında, finansal stres endeksinin belirlenmesi büyük önem taşımaktadır. Çalışma ile makine öğrenmesi yöntemleri kullanılarak finansal stres endeksi seviyesinin tahmin edilmesi amaçlanmaktadır. Bu amaçla finansal stres endeksinin haftalık zaman serileri, bağımsız ve hibrit modeller kullanılarak incelenmiştir. Yapay sinir ağları, bağımsız makine öğrenme modelleri olarak kullanılırken, hibrit modeller oluşturmak için bir ön işleme tekniği olarak dalgacık dönüşümü kullanılmıştır. Ayrıca, finansal stres endeksi tahminlerinde, model doğruluklarını artırmak için otokorelasyon fonksiyonlarını kullanarak gecikme uzunlukları elde edilmiştir. Çalışmanın bulguları, çeşitli performans göstergeleri açısından değerlendirilmiştir. Finansal stres endeksinin tahmin edilmesinde dalgacık dönüşümlü yapay sinir ağları modelinin, yalın yapay sinir ağları modelinden daha iyi performans sergilediği tespit edilmiştir. Çalışma sonuçlarının finansal stres endeksini takip eden araştırmacı ve uygulayıcılar için fayda sağlayacağı düşünülmektedir.  

References

  • Aghelpour, P., Mohammadi, B., & Biazar, S. M. (2019). Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoretical and Applied Climatology, 138(3), 1471-1480.
  • Akay, E. Ç., Topal, K. H., Kizilarslan, S. & Bulbul, H. (2019). Forecasting of Turkish housing price index: ARIMA, random forest, ARIMA-random forest. Pressacademia, 10(10), 7-11.
  • Aker, Y. (2012). Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model. Avrupa Bilim ve Teknoloji Dergisi, (35), 89-93.
  • Aksoy, B. (2021). Pay senedi fiyat yönünün makine öğrenmesi yöntemleri ile tahmini: Borsa İstanbul örneği. Business and Economics Research Journal, 12(1), 89-110.
  • Alsu, E. (2020). Finansal Stres İndeksi İle Doğrudan Yabancı Yatırımlar, Portföy Yatırımları Ve Dış Borç Stoku Arasındaki İlişki: ARDL Sınır Testi. ETÜ Sentez İktisadi ve İdari Bilimler Dergisi, (1), 27-40.
  • Arslankaya, S. & Toprak, Ş. (2021). Makine Öğrenmesi ve Derin Öğrenme Algoritmalarını Kullanarak Hisse Senedi Fiyat Tahmini. International Journal of Engineering Research and Development, 13(1), 178-192.
  • Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F. & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119.
  • Aygören, H., Saritaş, H. & Morali, T. (2012). İMKB 100 endeksinin yapay sinir ağları ve newton nümerik arama modelleri ile tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 4(1), 73-88.
  • Balakrishnan, R., Dysainger, S., Tytell, I. & Elekdag, S. A. (2009). The transmission of financial stress from advanced to emerging economies.
  • Balakrishnan, R., Dysainger, S., Elekdag, S. & Tytell, I. (2011). The transmission of financial stress from advanced to emerging economies. Emerging Markets Finance and Trade, 47(sup2), 40-68.
  • Başakın, E. E., Ekmekcioğlu, Ö., Çıtakoğlu, H. & Özger, M. (2022). A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Computing and Applications, 34(1), 783-812.
  • Bueno, S., & Salmeron, J. L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert systems with Applications, 36(3), 5221-5229.
  • Bülbül, H. & Akgül, I. (2018). Türkiye Finansal Stres Endeksi ve Markov Rejim Değişim Modeli ile Yüksek Stres Dönemlerinin Belirlenmesi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 13(3), 125-140.
  • Cardarelli, R., Elekdag, S. & Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, 7(2), 78-97.
  • Çamlica, F. & Gunes, D. (2016). Turkiye'de Finansal Stresin olculmesi: Yontemsel Bir Karsilastirma (No. 1606). Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
  • Dingli, A. & Fournier, K. S. (2017). Financial time series forecasting-a deep learning approach. International Journal of Machine Learning and Computing, 7(5), 118-122.
  • Ebadati, O. M. E. & Mortazavi, M. T. (2018). An efficient hybrid machine learning method for time series stock market forecasting. Neural Network World, 28(1), 41-55.
  • FRED, Federal Reserve Bank of St. Louis, St. Louis Fed Financial Stress Index (DISCONTINUED) [STLFSI2], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/STLFSI2, July, 2022.
  • Ghazanfar, M. A., Alahmari, S. A., Aldhafiri, Y. F., Mustaqeem, A., Maqsood, M., & Azam, M. A. (2017). Using machine learning classifiers to predict stock exchange index. International Journal of Machine Learning and Computing, 7(2), 24-29.
  • Hakkio, C. S. & Keeton, W. R. (2009). Financial stress: What is it, how can it be measured, and why does it matter. Economic Review, 94(2), 5-50.
  • Hoque, M. E. & Low, S. W. (2022). Reactions of Bitcoin and Gold to Categorical Financial Stress: New Evidence from Quantile Estimation. Risks, 10(7), 136.
  • Karacan, S. & Kırdar, M. (2021). Hisse Senedi Fiyat Tahmininde Makine Öğrenmesi ve Yapay Zeka Kullanımı. Journal of International Social Research, 14(76).
  • Kartal, C. (2020). Destek Vektör Makineleri ile Borsa Endekslerinin Tahmini. Itobiad: Journal of The Human & Social Science Researches, 9(2).
  • Kaynar, T., & Yiğit, Ö. E. (2021). Öznitelik Mühendisliği ile Makine Öğrenmesi Yöntemleri Kullanılarak BIST 100 Endeksi Değişiminin Tahminine Yönelik Bir Yaklaşım. Yaşar Üniversitesi E-Dergisi, 16(64), 1741-1762.
  • Duprey, T. & Klaus, B. (2017). How to Predict Financial Stress? An Assessment of Markov Switching Models (No. 2017-32). Bank of Canada.
  • Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. Journal of Financial Stability, 2(3), 243-265.
  • Luo, J., Chen, H., Xu, Y., Huang, H. & Zhao, X. (2018). An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling, 64, 654-668.
  • Monin, P. J. (2019). The OFR financial stress index. Risks, 7(1), 25.
  • Pabuçcu, H. (2019). Borsa Endeksi Hareketlerinin Makine Öğrenme Algoritmalari İle Tahmini. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 179-190.
  • Panchal, G., Ganatra, A., Kosta, Y. P. & Panchal, D. (2011). Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering, 3(2), 332-337.
  • Parray, I. R., Khurana, S. S., Kumar, M. & Altalbe, A. A. (2020). Time series data analysis of stock price movement using machine learning techniques. Soft Computing, 24(21), 16509-16517.
  • Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172.
  • Sandahl, J. F., Holmfeldt, M., Rydén, A. & Strömqvist, M. (2011). An index of financial stress for Sweden. S v ER ig ESR ik S bank, 2. Slingenberg, J. W. & De Haan, J. (2011). Forecasting financial stress. DNB Working Paper, No. 292/April.
  • Vdovychenko, A. & Oros, G. (2015). Financial stress index: estimation and application in empirical researches in Ukraine. Financial and Banking Services Market, Vol. 14 (No2).
  • Yiğiter, Ş. Y., Sarı, S. S. & Başakın, E. E. (2017). Hisse senedi kapanış fiyatlarının yapay sinir ağları ve bulanık mantık çıkarım sistemleri ile tahmin edilmesi.
There are 35 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Salim Sercan Sarı 0000-0003-2607-5249

Publication Date October 7, 2022
Submission Date August 9, 2022
Published in Issue Year 2022 Volume: 7 Issue: 3

Cite

APA Sarı, S. S. (2022). Finansal Stres Endeksinin Dalgacık Dönüşümlü Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi. The Journal of International Scientific Researches, 7(3), 282-296. https://doi.org/10.23834/isrjournal.1159770