You are here

Home » Content

Forecasting the BIST 100 Index Using Artificial Neural Networks with Consideration of the Economic Calendar

Journal Name:

  • International Review of Economics and Management

Publication Year:

  • 2016
DOI: 
10.18825/irem.67309

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Artificial Neural Networks (ANN) is an analysis method that mimics the operating principle of the human brain. The problem-solving skills and the high rate of success in solving complex problems of ANN, relative to the other traditional methods has made it a preference as well in the fields of finance and economics. I used Feed-forward back-propagation ANN (BPN-ANN) similar to related studies to forecast the indices, and employed trial-and-error method for the parameters like number of layers and neurons at layers to reach the optimal ANN structure. Extant literature has used important macroeconomic variables like inflation, interest rates and money supply to shape the future expected value of the dependent variables of the index. This study is followed a new and unique perspective from established literature to make use of the advantages of ANN to forecast on Turkish stock market index. Further, the study used the daily data between 29th July and 15th November of 2015 for BIST 100, including codified economic calendar events and major parities, dollar index and indices as variables. Due to the expected technical superiority of the system as a result of the nature of the variables to be used, a higher forecast performance is expected, even with the occurrence of events outside the scope, like important political developments. The expected results of this study, other than being an immense contribution to literature will be developed into an important tool that can be utilized by investors. Among several models, 18-20-1 structured MLP has best explanatory level with 0.893 R2 and 0.207 MSE values. This was followed the 18-16-1 structured MLP which had the minimum MSE as 0.025 and 0.88 R2. These are models 1 and 2 respectively. It is also observed that ECE (Economic Calendar Events) and ‘Other’ variables have notable effects that explain on the fluctuation of the index. Similarly, the two variables have shown their significant in other models as well. Prediction of opening is more successful than closing. ECE has greater success forecasting open prices.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-forecasting-bist-100-index-using-artificial-neural-networks-consideration-economic-calendar.pdf
  • 3
26
46
  • English

JEL Codes:

REFERENCES

References: 

Akay, EC & Gökdemir, T 2015, ‘The Comparison of The Financial Failure with Artificial Neural Network and Logit Models’, Journal of Business, Economics & Finance, vol. 4, no. 3, pp. 383-400.
Akcan, A & Kartal, C 2011, ‘İMKB Sigorta Endeksini Oluşturan Şirketlerin Hisse Senedi Fiyatlarının Yapay Sinir Ağları İle Tahmini’, Muhasebe ve Finansman Dergisi, July 2011, pp. 27-40.
Akçalı, BY 2015, ‘MIST Ülkeleri Finansal Baskı Endeksleri (FBE)'nin Yapay Sinir Ağları ve Box-Jenkins Yöntemleriyle Tahmin Edilerek Finansal Krizlerin Öngörülmesi’, Muhasebe Bilim Dünyası Dergisi, vol. 17, no. 2, pp. 347-384.
Akel, V & Karacameydan, F 2012, ‘Yatırım Fonları Net Varlık Değerlerinin Yapay Sinir Ağları Yöntemiyle Tahmin Edilmesi’, Anadolu Üniversitesi Sosyal Bilimler Dergisi, vol. 12, no. 2, pp. 87-105.
Ataseven, B 2013, ‘Yapay Sinir Ağları İle Öngörü Modellemesi’, Marmara Üniversitesi Sosyal Bilimler Enstitüsü Öneri Dergisi, vol. 10, no. 39, pp. 101-115.
Aygören, H, Sarıtaş, H & Moralı, T 2012, ‘İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile Tahmini’, International Journal of Alanya Faculty of Business, vol. 4, no. 1, pp. 73-88.
Benli, YK & Yıldız, A 2014, ‘Altın Fiyatının Zaman Serisi Yöntemleri ve Yapay Sinir Ağları İle Öngörüsü’, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, no. 42, pp. 213-224.
Chen, JH, Diaz, JF & Huang, YF 2013, ‘High Technology ETF Forecasting: Application of Grey Relational Analysis and Artificial Neural Networks’, Frontiers in Finance and Economics, vol. 10, no. 2, pp. 129-155.
Ekinci, Y, Temur, GT, Çelebi D & Bayraktar D 2008, ‘Ekonomik Kriz Döneminde Firma Başarısı Tahmini: Yapay Sinir Ağları Tabanlı Bir Yaklaşım’, Endüstri Mühendisliği Dergisi, vol. 21, no. 1, pp. 17-29.
Erilli, NA, Eğrioğlu, E, Yolcu, U, Aladağ, CH & Uslu, VR 2010, ‘Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı İle Öngörüsü’, Doğuş Üniversitesi Dergisi, vol. 11, no. 1, pp. 42-55.
Fadlalla, A & Amani, F 2014, ‘Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks’, Intelligent Systems in Accounting, Finance andManagement, no. 21, pp. 209-223.
Hamid, SA & Habib, A 2014, ‘Financial Forecasting with Neural Networks’, Academy of Accounting and Financial Studies Journal, vol. 18, no. 4, pp. 37-55.
Jin, J & Kim, J 2015, ‘Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks’, Plos One, vol. 10, no. 11, pp. 1-23.
Ş. Telli-M. Coşkun/ IREM, 4 (3), 2016, 26-46. 42
Karahan, M 2015, ‘Yapay Sinir Ağları Metodu ile İhracat Miktarlarının Tahmini: Arıma ve YSA Metodunun Karşılaştırmalı Analizi’, Ege Akademik Bakış, vol. 15, no. 2, pp. 165-172.
Karymshakov, K & Abdykaparov, Y 2012, ‘Forecasting Stock Index Movement with Artifıcial Neural Networks: The Case of Istanbul Stock Exchange’, Trakya Üniversitesi Sosyal Bilimler Dergisi, vol. 14, no. 2, pp. 231-242.
Kutlu, B & Badur, B 2009, ‘Yapay Sinir Ağları ile Borsa Endeksi Tahmini’, İstanbul Üniversitesi İşletme İktisadi Enstitüsü Dergisi-Yönetim, vol. 20, no. 63, pp. 25-40.
Merh, N 2013, ‘Optimal Model Design of Artificial Neural Networks for Forecasting Indian Stock Trends: An Experimental Approach’, XIMB Journal of Management, vol. 10, no. 2, pp. 21-42.
Siddiqui, TA & Abdullah, Y 2015, ‘Developing A Nonlinear Model to Predict Stock Prices In India: An Artificial Neural Networks Approach’, The IUP Journal of Applied Finance, vol. 21, no. 3, pp. 36-49.
Toraman, C 2008, ‘Demir-Çelik Sektöründe Yapay Sinir Ağları ile Hisse Senedi Fiyat Tahmini: Erdemir A.Ş. ve Kardemir A.Ş. Üzerine Bir Tahmin Uygulaması’, Muhasebe ve Finansman Dergisi, no. 39, pp. 44-57.
Tosunoğlu, NG & Benli, YK 2012, ‘Morgan Stanley Capital International Türkiye Endeksinin Yapay Sinir Ağları İle Öngörüsü’, Ege Akademik Bakış, vol. 12, no. 4, pp. 541-547.
Ulusoy, T 2010, ‘İMKB Endeks Öngörüsü için İleri Beslemeli Ağ Mimarisine Sahip Yapay Sinir Ağı Modellemesi’, International Journal of Economic and Administrative Studies, vol. 3, no. 5, pp. 21-40.
Ünlü, U, Yıldız, B & Yalama, A 2009, ‘İlk Halka Arzlarda Uzun Dönem Getirilerinin Tahmini: Yapay Sinir Ağları İle İMKB için Ampirik Bir Çalışma’, Ekonometri ve İstatistik, no. 10, pp. 29-47.
Yakut, E, Elmas, B & Yavuz, S 2014, ‘Yapay Sinir Ağları ve Destek Vektör Makineleri Yöntemleriyle Borsa Endeksi Tahmini’, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 19, no. 1, pp. 139-157.

Thank you for copying data from http://www.arastirmax.com