SavedAdd your journal

Journals / Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi / 2019 / Cilt: 41 - Sayı: 2

ANALYSIS OF CHANGES IN İSTANBUL CITY INDEX VALUES WITH HIDDEN MARKOV MODEL

Öyküm Esra AŞKIN
Pages
319–337
Publication Date
2020-01-06
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The model is constructed with four exogenous factors such as exchange rate, interest rate, money supply and consumer price index and the validity of model is shown by one-, two – and three-months ahead successful prediction results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:City indexes are not only a significant indicator of regional development but also a very useful guide for decision makers interested in investment to a specific region.
📊
Key Findings & Data:Different city indexes have been calculated by İstanbul Stock Exchange (ISE) in order to reflect the financial performances of cities.
💡
Conclusion & Impact:The model is constructed with four exogenous factors such as exchange rate, interest rate, money supply and consumer price index and the validity of model is shown by one-, two – and three-months ahead successful prediction results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:City Indexes · İstanbul City Index · Hidden Markov Model

Özet

City indexes are not only a significant indicator of regional development but also a very useful guide for decision makers interested in investment to a specific region. Different city indexes have been calculated by İstanbul Stock Exchange (ISE) in order to reflect the financial performances of cities. The main purpose of this study is to predict the future behaviors of İstanbul city index which has the highest share of stocks being traded on ISE. To achieve this purpose, an important pattern recognition technique that produces reliable estimates, Hidden Markov model (HMM), is suggested. The model is constructed with four exogenous factors such as exchange rate, interest rate, money supply and consumer price index and the validity of model is shown by one-, two – and three-months ahead successful prediction results.

🔖

Cite This Article

AŞKIN, Ö. E. (2020). ANALYSIS OF CHANGES IN İSTANBUL CITY INDEX VALUES WITH HIDDEN MARKOV MODEL. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 41 - Sayı: 2, 319–337. https://ajindex.com/articles/6320

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. AKSOY, M. (2013). İstanbul Menkul Kıymetler Borsası’nda Finansal Kriz Döneminde Yabancı Yatırımcıların Hisse Senedi Tercihlerinin Analizi, İÜ Siyasal Bilgiler Fakültesi Dergisi, 48: 135-150.
  2. ANGELIS, L.D., Paas, L.J. (2013). A Dynamic Analysis of Stock Markets Using a Hidden Markov Model, Journal of Applied Statistics, 40(8): 1682-1700.
  3. ATMACA, V.D. (2018). BİST Şehir Endeksleri Oynaklığının DCC-GARCH Model ile Analizi, Yönetim Bilimleri Dergisi, 16(31): 287-308.
  4. AYAZ, O., Alp, S. (2018). Saklı Markov Modeli Kullanılarak İstanbul’daki Üniversite Öğrencilerinin GSM Operatör Tercihlerini Etkileyen Faktörlerin Analizi, Çukurova Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 33(4): 203-212.
  5. AYAZ, O., Alp, S. (2018). Saklı Markov Modeli Kullanılarak İstanbul’daki Üniversite Öğrencilerinin GSM Operatör Tercihlerini Etkileyen Faktörlerin Analizi, Çukurova Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 33(4): 203-212.
  6. BAYRAKDAROĞLU, A., Tepeli, Y. (2018). BİST Şehir Endekslerinin Risk-Getiri Analizi Üzerine Bir İnceleme, Muhasebe ve Finansman Dergisi, 80: 147-160.
  7. BAYRAMOĞLU, M.F., Pekkaya, M. (2010). İMKB Tarafından Hesaplanan Endekslerde Yeni Gelişmeler ve İMKB Şehir Endeksleri, Muhasebe ve Finansman Dergisi (45): 200-215.
  8. BHAR, R., Hamori, S. (2004). Hidden Markov Models Applications to Financial Economics, Netherlands: Kluwer Academiz Publishers.
  9. BICEGO, M., Murino, V. (2004). Investigating Hidden Markov Models’ Capabilities in 2D Shape Classification, IEEE Transactions On Pattern Analysis and Machine Intelligence, 26(2): 281-286.
  10. CAN, T., Öz, E. (2009). Saklı Markov Modelleri Kullanılarak Türkiye’de Dolar Kurundaki Değişimin İncelenmesi, İstanbul Üniversitesi İşletme Fakültesi Dergisi, 1: 1-23.
  11. CAN, T., Öz, E. (2009). Marka Tercihlerine ve Tercih Nedenlerine Gizli Markov Modelinin Uygulanması, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 10(2): 167-185. CENTRAL BANK OF THE REPUBLİC OF TURKEY Web Page, https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket, (Access Date: 29.01.2019).
  12. ÇAKIR, Z. (2016). Şehir Endekslerinin Finansal Performanslarının Ölçülmesi ve Değerlendirilmesi, (Yayımlanmamış Yüksek Lisans Tezi), Hitit Üniversitesi,
  13. Sosyal Bilimler Enstitüsü, Çorum. DAĞLIOĞLU, C., Kıral, G. (2018). Hisse Senedi Piyasa Fiyatlarının Saklı Markov Modeli ile Tahmin Edilmesi, Uluslararası Ekonomi ve Yenilik Dergisi, 4(1): 61-75.
  14. DUAN, J., Wang, W., Zeng, J., Zhang, D., Shi, B. (2009). A Prediction Algorithm for Time Series Based on Adaptive