Prediction of International Stock Markets Based on Hybrid Intelligent Systems

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

Salim Lahmiri (University of Quebec at Montreal, Canada & ESCA School of Management, Morocco)
DOI: 10.4018/978-1-5225-0788-8.ch064
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This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.
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Since trading financial assets is a highly risky task, investors and portfolio managers need accurate predicting systems. However, financial assets time series are nonlinear and non-stationary. Therefore, they mostly follow a noisy path. The nonlinearity implies that asset prices do not follow a linear pattern, and non-stationary implies that their dynamics change over time. As a result, sophisticated systems were proposed to model the underlying financial time series by capturing the above patterns in order to provide accurate forecasts (Lahmiri, 2013, 2014a, 2014b, 2014c; Lahmiri & Boukadoum, 2014a, 2014b; Lahmiri, Boukadoum, & Chartier, 2014a, 2014b; Lahmiri & Boukadoum, 2015a, 2015b). In the 2000s, nonlinear and adaptive forecasting models such as artificial neural networks (ANNs) have become popular intelligent systems widely used in stock market modeling and forecasting (Zimmermann, Neuneier & Grothmann, 2001; Yao & Tan, 2000; Atsalakis & Valavanis, 2009; Huang & Wu, 2010; Hsieh, Hsiaso & Yeh, 2011; Wang et al, 2011). In general, previous works (Zimmermann, Neuneier & Grothmann, 2001; Yao & Tan, 2000; Atsalakis & Valavanis, 2009a) have shown that ANNs were superior to traditional statistical models such as the well known auto-regressive integrated moving average (ARIMA) models (Box & Jenkins, 1970). Indeed, ARIMA models are based on the assumptions that the time series are stationary and that the errors of the model are normally distributed. Unfortunately, financial data do not meet those criteria and, as a result, the techniques based on statistical approaches could not provide accurate financial forecasts. However, the ANN systems suffer several disadvantages, namely dependency on network architecture, type of transfer function, parameters choice, and being considered as a black-box.

To overcome these disadvantages, hybrid soft computing systems were proposed in the literature. These systems combine synergistically ANNs and other soft computing models to obtain complementary hybrid intelligent system models. For instance, genetic algorithms (Goldberg, 1989) were proposed to automatically optimize the topology and parameters of ANNs (Yao, 1999), and fuzzy logic (Zadeh, 1965) was incorporated into ANN resulting in adaptive neuro-fuzzy inference system (ANFIS) (Jang, 1993). In particular, in one hand ANN is capable to recognize patterns and adapt to data. On the other hand, fuzzy inference systems incorporate human knowledge and expertise to make fuzzy inference and decision (Jang, 1993; Atsalakis & Valavanis, 2009b).

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