Open AccessPredicting the Istanbul Stock Exchange Index Return using Technical Indicators
Author(s) -
Şenol Emir
Publication year - 2013
Publication title -
international journal of finance and banking studies
Language(s) - English
Resource type - Journals
ISSN - 2147-4486
DOI - 10.20525/ijfbs.v2i3.158
Subject(s) - mean squared error , mean absolute percentage error , support vector machine , statistics , mathematics , index (typography) , stock exchange , regression , linear regression , econometrics , hyperparameter optimization , mean absolute error , coefficient of determination , computer science , economics , machine learning , finance , world wide web
The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.