Prediction of Investment Alternatives with Artificial Neural Network
Prediction of Investment Alternatives with Artificial Neural Network
Purpose – Since investment decisions are made in an uncertain environment, it is vital to develop prediction models that enable investors to make the right decision on time. Artificial neural network (ANN) method is one of the most widely used for this purpose. Thus, in the study, USA dollar Exchange rate, BIST 100 index, gold price in ounce and TL deposit interest rate are determined as alternative investments and their future values are predicted by using ANN models. The lag values of each investment alternative with other investment alternative values are considered as influencing variables. Hence, it is aimed to develop multidimensional prediction models. Design/methodology/approach – In the study, a multilayer artificial neural network model was used. As a data set obtained from the Central Bank database, 284 weekly data for the period of January 2015 and June 2020 were included in the analysis. Of this data set, 238 were used for training and 46 for testing. In the models, the lagged values of each variable and the influencing variables are included in the model as independent variables. Model trials were carried out over the hyperbolic tangent and logistic activation functions for each variable. As the error function, the sum of the squares of the error was chosen. The fast back propagation algorithm was used as the learning algorithm. Findings – ANN models were built with the dataset and processed with algorithm specified in the method part. Prediction values for each investment alternative were obtained by choosing the model with the smallest mean squares error among constructed all the models. The fact that the chosen prediction model results in very low error rates reveals that the prediction performances of the models are quite well. In addition, obtaining over 93% R2 values indicating the explanatory power of these models implies the validity of the them. Discussion – Predicting the future value of alternative investments for investors minimizes the possible risks they may encounter. Developing models such as neural networks by identifying appropriate influencing variables provides investors to do this. This study developed multidimensional models by analyzing both the relationship between alternative investments and their own lagged values. As a result, the lagged values of investment alternatives were found to be effective. This result reflects the situation that supports the assumption that the structure shown in the past will continue in the future. In fact, this imply that the effects of causal variables have been already reflected in their past values. Hence, it can be stated that ANN models based on time series data might be more preferable than models using influencing data. However, in this case, the break points or periods specific to time series analysis should be taken into account and the autocorrelation problem should be considered. Thus, it will be ensured that the prediction performance of the models emerges with better results. In addition, the better prediction values can get in shorter time by generating models with optimal parameter values.
___
- Akel, V., & Karacameydan, F. (2012). Yatırım Fonları Net Varlık Değerlerinin Yapay Sinir Ağları Yöntemiyle Tahmin Edilmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 12(2), 87-106.
- Akyurt, İ. Z. (2015). Talep Tahmininin Yapay Sinir Ağlarıyla Modellenmesi: Yerli Otomobil Örneği. Ekonometri ve İstatistik(23), 147-157.
- Ataseven, B. (2013). Yapay Sinir Ağları İle Öngörü Modellemesi. Öneri, 10(39), 101-115.
- Aygören, H., Sarıtaş, H., & Moralı, T. (2012). İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile Tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 4(1), 73-88.
- Barkhatov, N. A., Revunov, S. E., Smirnova, Z. V., Cherney, O. T., & Katkova, O. V. (2020). Using Neural Network for forecasting in the Financial Sector. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 3121-3124.
- Benli, Y. K., & Yıldız, A. (2014). Altın Fiyatının Zaman Serisi Yöntemleri Ve Yapay Sinir Ağları İle Öngörüsü. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi(42).
- Çalışkan, M. M., & Deniz, D. (2015). Yapay Sinir Ağlarıyla Hisse Senedi Fiyatları ve Yönlerinin Tahmini. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 10(3), 177-194.
- Çınaroğlu, E., & Avcı, T. (2020). THY Hisse Senedi Değerinin Yapay Sinir Ağları İle Kestirimi. Atatürk Üniversitesi, İktisadi ve İdari Bilimler Dergisi, 34(1).
- Çuhadar, M. (2013). Türkiye’ye Yönelik Dış Turizm Talebinin MLP, RBF Ve TDNN Yapay Sinir Ağı Mimarileri İle Modellenmesi Ve Tahmini: Karşılaştırmalı Bir Analiz. Journal of Yasar University, 8(31), 5274-5295.
- Es, H. A., Kalender, F. Y., & Hamzaçebi, C. (2014). Yapay Sinir Ağları İle Türkiye Net Enerji Talep Tahmini. Gazi Üniv. Müh. Mim. Fak. Der., 29(3), 495-504.
- Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction. Neurocomputing,, 172, 446- 452.
- Gradojevic, N., & Yang, J. (2000). The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables. Bank Of Canada Working Paper.
- Haykin, S. (1999). Neural Networks A Comprehensive Foundation. Prentice – Hall.
- Hosaka, T. (2018). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 09(39).
- İnce, H., & Çakır, F. S. (2017). Model Melezleme İle Finansal Zaman Serisi Analizi. Journal of Economics, Finance and Accounting, 4(3).
- Kaastra, I., & Boyd, M. (1996). Designing a Neural Network for Forecasting Financial and Economic Time Series. Neurocomputing, 10, 215-236.
- Kantar, L. (2019). BİST100 Endeksinin Yapay Sinir Ağları Ve ARMA Modeli İle Tahmini. 23. Uluslararası Finans Sempozyumu.
- Karahan, M. (2015). Turizm Talebinin Yapay Sinir Ağaları Yöntemiyle Tahmin Edilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 20(2), 195-209.
- Kaynar, O., Taştan, S., & Demirkoparan, F. (2011). Yapay Sinir Ağları İle Doğalgaz Tüketim Tahmini. Atatürk Ü. İİBF Dergisi, 463-474.
- Kocatepe, C. İ., & Oktay. Y. (2016). Ekonomik Endeksler Kullanılarak Türkiye’deki Altın Fiyatındaki Değişim Yönünün Yapay Sinir Ağları İle Tahmini. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 4, 926-934.
- Nwokike , C. C., Chukwuma , B., Maxwell, O., Uche-Ikonne, O. O., Offorha, B. C., & Ukomah, H. I. (2020). Forecasting Monthly Prices of Gold Using Artificial Neural Network. Journal of Statistical and Econometric Methods, 9(3), 19-28.
- Özkan, F. (2011). Döviz Kuru Tahmininde Yapay Sinir Ağlarıyla Alternatif Yaklaşım. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 6(2), 185-200.
- Persio, L., & Honchar, O. (2016). Artificial Neural Networks architectures for stock price prediction: comparisons and applications. Internatıonal Journal Of Circuits, Systems And Signal Processing, 10.
- Polat, Ö., & Temurlenk, S. (2011). Yapay Sinir Ağları Metodolojisi ile Makroekonomik Zaman Serilerinde Ongoru Modellemesi. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 1(2), 98-106.
- Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1).
- Şahin, E. E. (2018). Kripto Para Bitcoin: ARIMA ve Yapay Sinir Ağları İle Fiyat Tahmini. Fiscaoeconomia, 2(2), 74-92.
- Şeker, M., Yıldırım, E. S., & Berkay, A. (2004). Yapay Sinir Ağlarının Ekonomik Tahminlerde Kullanılması. Pamukkale Üniversitesi Mühendislik Fakültesi, 10(Özel Sayı), 79-83.
- Warner, B., & Misra, M. (1996). Understanding Neural Networks As Statistical Tool. The American Statistician, 50(4).
- Yavuz, U., Özen, Ü., Taş, K., & Çağlar, B. (2020). Yapay Sinir Ağları ile Blockchain Verilerine Dayalı Bitcoin Fiyat Tahmini. Bilişim Sistemleri ve Yönetim Araştırmaları Dergisi, 2(1), 1-9.
- Yıldız, A., & Yıldız, D. (2014). BIST 100 Endeksi’nin ARIMA ve Yapay Sinir Ağlarına Dayalı Karma Yöntem İle Tahmini. Finans, Politik ve Ekonomik Yorumlar, 51(588).
- Yıldız, D. (2009). Zaman Serileri Analizi ve Yapay Sinir Ağları ile Tahmin: Yabancı Portföy Yatırımları Üzerine Uygulama. Ankara Üniversitesi Sosyal Bilimler Enstitüsü, Yayımlanmamış Doktora Tezi.
- Yıldız, D. (2021). Koronavirüs Pandemisi Döneminde Tüketici Kredi Faiz Oranının Yapay Sinir Ağları ile Öngörülmesi. International Symposium on Business, Economics, and education ISBE 2021 (pp. 192-204). Ankara: Gazi Kitabevi.
- Yu, P., & Yan, X. (2019). Stock price prediction based on deep neural networks. Neural Computing and Applications.
- Yüksel, R., & Akkoç, S. (2016). Altın Fiyatlarının Yapay Sinir Ağları İle Tahmini Ve Bir Uygulama. Doğuş Üniversitesi Dergisi, 17(1), 39-50.
- Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
- Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116(1), 16- 32.