A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index

Autor:	Alaa Sheta, Sara Elsir M. Ahmed, Hossam Faris
Rok vydání:	2015
Předmět:	Stock market prediction Artificial neural network Computer science business.industry Machine learning computer.software_genre Stock market index Regression Support vector machine Linear regression Stock market Artificial intelligence business computer Predictive modelling
Zdroj:	International Journal of Advanced Research in Artificial Intelligence. 4
ISSN:	2165-4069 2165-4050
DOI:	10.14569/ijarai.2015.040710
Popis:	Obtaining accurate prediction of stock index sig-nificantly helps decision maker to take correct actions to develop a better economy. The inability to predict fluctuation of the stock market might cause serious profit loss. The challenge is that we always deal with dynamic market which is influenced by many factors. They include political, financial and reserve occasions. Thus, stable, robust and adaptive approaches which can provide models have the capability to accurately predict stock index are urgently needed. In this paper, we explore the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index. We will also show how traditional models such as multiple linear regression (MLR) behave in this case. The developed models will be evaluated and compared based on a number of evaluation criteria.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f052cf7752a10d2d99c4b494e202acda https://doi.org/10.14569/ijarai.2015.040710 Zobrazit plný text záznamu