Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model

Author:

Wang Feiyu

Abstract

The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy.

Publisher

EDP Sciences

Reference10 articles.

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