Abstract
Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.
Highlights
Forecasting stock market behavior is quite uncertain due to high market volatility, nonlinearity, their complex dynamic systems, and the time-varying nature of markets
In order to avoid the biases of the neural-based models, 20 simulations are conducted, and the averaged results are collected for comparison purposes
Since only one new closing price data point is included into the training set through each move of the sliding window over the financial time series, there may not be a significant change in the nonlinear behavior of the training data set
Summary
Forecasting stock market behavior is quite uncertain due to high market volatility, nonlinearity, their complex dynamic systems, and the time-varying nature of markets. Markets will respond arbitrarily to changes in the current political climate and other macroeconomic factors (Hsu et al, 2016; Kotha & Sahu, 2016). These characteristics of stock market must be captured and accounted for in models in order to establish intelligent techniques for forecasting market prices. Stock market researchers focus on developing models/ methodologies to effectively forecast prices, with the goal of maximizing profits through appropriate trading strategies. In reality, this is a critical, demanding, and challenging job
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
