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.
Reference10 articles.
1. Mehta Anukrati. “A Comprehensive Guide to Types of Neural Networks. ” 25 Jan. 2019, www.digitalvidya. com/blog/types-of-neuralnetworks/.
2. Hegazy Osman., Omar S.Soliman., and Mustafa Abdul Salam. “A machine learning model for stock market prediction. ” arXiv preprint arXiv:1402.7351 (2014).
3. Schulenburg S. and Ross P., “Explorations in LCS models of stock trading, ” Advances in Learning Classifier Systems, 2001, pp. 151-180.
4. Wolfe R.K., “Turning point identification and Bayesian forecasting of a volatile time series, ” Computers and Industrial Engineering, 1988, pp 378-386.
5. Altay Erdinc., and Hakan Satman M.. “Stock market forecasting: artificial neural network and linear regression comparison in an emerging market. ” Journal of Financial Management & Analysis 18. 2 (2005): 18.
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