A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index
By Alaa F. Sheta, Sara Ahmed, Hossam Faris. 2015
134 citations recorded by the source corpus.
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.
Subfields: Electrical and Electronic Engineering, Management Science and Operations Research
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Citations in the corpus
- Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, methodology): “The third one is support vectormachines (SVM).”
- Fintech Model: The Random Neural Network with Genetic Algorithm (2018, methodology): “et al [16] compares Regression, Artificial Neural Networks and Support Vector Machines for predicting the S&P 500 Stock Market Price Index.”