Forecasting S&P 500 index using artificial neural networks and design of experiments

By Seyed Taghi Akhavan Niaki, Saeid Hoseinzade. 2013

148 citations recorded by the source corpus.

The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Weiwei Jiang
2. Sara Ahmed
3. Alaa F. Sheta
4. Saman Haratizadeh
5. Hossam Faris
6. Ehsan Hoseinzade
7. Will Serrano

In Management Science and Operations Research

1. Dixing Xu
2. Weiwei Jiang
3. Zhengyao Jiang
4. Jinjun Liang
5. Peng Ding
6. Weiyu Si
7. Sara Ahmed
8. Alaa F. Sheta

Citations in the corpus

• Classification-based financial markets prediction using deep neural networks (2016, neutral): “As such, there has been a recent resurgence in the method, in part facilitated by advances in modern computer architecture (Chen et al., 2013; Niaki and Hoseinzade, 2013; Vanstone and Hahn, 2010).”
• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017, background): “However, many of them try to predict price movements or trends (Heaton et al., 2016; Niaki and Hoseinzade, 2013; Freitas et al., 2009).”
• CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, background): “Other useful variables: According to different papers, other variables including Treasury bill rates, The term and default spreads have shown to be useful in stock market prediction (Zhong & Enke, 2017; Niaki & Hoseinzade, 2013; Enke & Thawornwong, 2005).”
• A Multi-objective Deep Reinforcement Learning Approach for Stock Index Future’s Intraday Trading (2017, background): “Many researches have been done on machine learning approaches to financial market trading, but many of them try to predict price movement or trends [2], [3].”
• Applications of deep learning in stock market prediction: recent progress (2020, methodology): “Table 10 – continued from previous page Metrics Article List Accuracy de Oliveira et al. (2013); Niaki & Hoseinzade (2013); Ding et al. (2014, 2015); Chen et al. (2017); Dingli & Fournier (2017); Huynh et al. (2017); Li & Tam (2017); Liu & Song (2017); Nelson et al. (2017); Selvin et al. (2017);…”
• A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, methodology): “Variations of ANNs can be used to perform classification, pattern recognition and predictive tasks [15], [19], [20], [22], [30].”