Forecasting the returns of pension investment funds in Turkey with artificial neural network
Abstract
Individuals start to experience the retirement period after completing their active working time. During the retirement period, the income generated during the work period is reduced. The Personal Pension System was organized on the basis that both individuals can able to generate additional income during the retirement period and the savings are increased and remain in the system. This system aims to enable individuals to increase their income during the retirement period through their savings.
Funds operated according to the religious property principles created to drive investment into savings accumulated in individual pension accounts of participants seeking to retire and build up wealth are called pension funds. Pension investment funds are of great importance to our capital market and the future of our country.
Funds operated according to the religious property principles created to drive investment into savings accumulated in individual pension accounts of participants seeking to retire and build up wealth are called pension funds. Pension investment funds are of great importance to our capital market and the future of our country.
Keywords
Pension Mutual Funds, Artificial Neural Networks, Financial Forecasting
References
- [1] Emeklilik Gözetim Merkezi, Bireysel-Emeklilik-Nedir? https://www.egm.org.tr/bireysel-emeklilik/bireysel-emeklilik-nedir (2020).
- [2] Korkmaz T., Türkiyede’ki Emeklilik Fonlarının Perfonmans Ölçümü ve Fon Yöneticilerinin Zamanlama Yeteneği, Akdeniz Üniversitesi İ.B.F Dergisi, (14) (2007) 66-93.
- [3] Zor İ., OECD Ülkeleri ve Türkiye Bağlamında Bir Değerlendirme, Reasürör Dergisi, 70 (2008) 6-20.
- [4] Qi M., Zhang P.G., An Investigation of Model Selection Criteria for Neural Network Time Series Forecasting, European Journal of Operational Research, 132 (3) (2001) 666-680.
- [5] Eğrioğlu E., Aladağ Ç.H., Yapay Sinir Ağları ve Arıma Modellerin Melez Yaklaşımı ile Zaman Serilerinde Öngörü, İstanbul Üniversitesi VII. Ulusal Ekonometri ve İstatistik Sempozyumu, 26-27 Mayıs 2005, İstanbul.
- [6] Avcı E., Forecasting Daily and Sessional Returns of the ISE-100 Index with Neural Networks Models, Doğuş Üniversitesi Dergisi, 8 (2) (2007) 128-142.
- [7] Wallace M.P., Neural Networks And their Application to Finance, Business Intelligence Journal, (July) (2008) 67-76.
- [8] Düzgün R., A Comparison of Artificial Neural Networks and ARIMA Models’ Success in GDP Forecast, Marmara Üniversitesi İ.İ.B.F. Dergisi, 25 (2) (2008) 165-176.
- [9] Nitin N.M., Saxena V. P., Pardasani, K. R., Next Day Stock Forecasting An Application of ANN and ARIMA, The IUP, International Journal of Theoretical and Applied Finance, 17 (1) (2011) 70-84.
- [10] Irmak S., Köksal C.D., Asilkan Ö., Hastanelerin Gelecekteki Hasta Yoğunluklarının Veri Madenciliği Yöntemleri ile Tahmin Edilmesi, Uluslararası Alanya İşletme Fakültesi Dergisi, 4 (1) (2012) 101-114.