Stock index forecasting based on a hybrid model

By Jujie Wang, Jianzhou Wang, Zhe George Zhang, Shu-Po Guo. 2012

329 citations recorded by the source corpus.

Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network (BPNN) can be used to make forecasts based on time series. In this paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the proposed hybrid model (PHM) is determined by genetic algorithm (GA). The closing of the Shenzhen Integrated Index (SZII) and opening of the Dow Jones Industrial Average Index (DJIAI) are used as illustrative examples to evaluate the performances of the PHM. Numerical results show that the proposed model outperforms all traditional models, including ESM, ARIMA, BPNN, the equal weight hybrid model (EWH), and the random walk model (RWM).

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

View Paper: https://www.semanticscholar.org/paper/dac54e123622f352dcde7ef9aa522b341618565e

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. Sima Siami‐Namini
2. Akbar Siami Namin
3. M. Omair Shafiq
4. Jingyi Shen
5. Neda Tavakoli

In Management Science and Operations Research

1. S. Al Wadi
2. Efstratios F. Georgopoulos
3. Georgios Sermpinis
4. Andreas S. Karathanasopoulos
5. Christian L. Dunis
6. Konstantinos A. Theofilatos
7. M. Almasarweh
8. M. Ugur Gudelek

Citations in the corpus

• Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization (2013, background): “Section 5 gives the empirical results of all the models considered and investigates the possibility of improving their performance with the introduction of a sophisticated trading strategy while section 6 provides some concluding remarks.”
• Stock Price Prediction Using the ARIMA Model (2014, background): “Stock price prediction is regarded as one of most difficult task to accomplish in financial forecasting due to complex nature of stock market [1, 2, 3].”
• ARIMA Model in Predicting Banking Stock Market Data (2018, background): “Banking time series data and financial time series in general is very hard in decomposition and forecasting because the data are non-stationary and non-linear with high heteroscedasticity (Pai & Lin, 2005; Wang et al., 2012; Wei, 2013).”