Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models

Abstract

Especially for the last decade, the neural network models have been applied to solve financial problems like portfolio construction and stock market forecasting. Among the alternative neural network models, the multilayer perceptron models are expected to be effective and widely applied in financial forecasting. This study examines the forecasting power multilayer perceptron models for daily and sessional returns of ISE-100 index. The findings imply that the multilayer perceptron models presented promising performance in forecasting the ISE-100 index returns. However, further emphasis should be placed on different input variables and model architectures in order to improve the forecasting performances.

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

“…These studies used various types of ANN to predict accurately the stock price return and the direction of its movement. ANN has been demonstrated to provide promising results in predict the stock price return (Avci, 2007;Egeli, Ozturan, & Badur, 2003;Karaatli, Gungor, Demir, & Kalayci, 2005;Kimoto, Asakawa, Yoda, & Takeoka, 1990;Olson & Mossman, 2003;White, 1988;Yoon & Swales, 1991).. Leung et al (2000) examined various prediction models based on multivariate classification techniques and compared them with a number of parametric and nonparametric models which forecast the direction of the index return. Empirical experimentation suggested that the classification models (discriminant analysis, logit, probit and probabilistic neural network) outperform the level estimation models (adaptive exponential smoothing, vector auto regression with Kalman filter updating, multivariate transfer function and multilayered feed forward neural network) in terms of predicting the direction of the stock market movement and maximizing returns from investment trading.…”

Fuzzy-neural model with hybrid market indicators for stock forecasting

“…Tsanga et al (2007) technical indicators with ANN was utilised to created trading alert system and their findings showed that ANN can effectively guide investors when to buy or sell stocks. Avci (2007) also used technical indicators with ANN, and his finding demonstrated that ANN could be used effectively to forecast daily and sessional returns of the Ise-100 Index.…”

Büyük Dünya Endeksleri Kullanılarak BIST-100 Endeksi Değişim Yönünün Makine Öğrenmesi Algoritmaları ile Sınıflandırılması

“…Örneğin Diler (2003), BIST-100 endeksinin bir sonraki günkü yönünü YSA kullanarak %60,81 doğruluk oranı ile tahmin etmiştir [21]. Avcı (2007) da çalışmasında BIST-100 endeksinin seanslık ve günlük getirilerini YSA ile tahmin etmiştir. Çalışma sonucunda seanslık tahminin daha başarılı sonuç ürettiğini belirtmiştir [22].…”