TY  - JOUR
T1  - Improving Stock Index Predictability of Machine Learning Algorithms with Global Cues
AU - Subha, M.V. AU - Sulochana, Arul AU - Nambi, Thirupparkadal 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 2
SP  - 329
EP  - 337
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.329.337
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.329.337
KW  - Stock index prediction
KW  -support vector machines
KW  -case based reasoning
KW  -decision trees
KW  -machine learning
KW  -neural network
AB  - The performance of predictive models depends more on predictor sets than the efficiency of the predictive algorithm. This study investigates the role of global cues as input predictors in improving the predictability of the models. It also attempts to study the efficacy of various machine learning algorithms such as support vector machines, case-based reasoning, decision trees and artificial neural network in forecasting the stock indices. It focuses on studying predictability of the widely-followed Indian stock market indices BSE SENSEX and CNX Nifty using the above-mentioned four machine learning algorithms with two separate set of predictors, a set of commonly used technical indicators and another set of daily global cues such as gold price, crude oil price, exchange rate of strong currencies, LIBOR and close price of major global stock market indices. With its lowest forecasting error values, SVM outperforms other predictive models in terms of all key performance metrics. Among the predictor sets, global cues show a higher level of predictive accuracy.
ER  -