Abstract
BackgroundImproving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. Given its direct impact on related decisions, various attempts have been made to achieve more accurate and reliable forecasting results, of which the combining of individual models remains a widely applied approach. In general, individual models are combined under two main strategies: series and parallel. While it has been proven that these strategies can improve overall forecasting accuracy, the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model.MethodsTherefore, this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one.ResultsAccordingly, the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price. To do so, autoregressive integrated moving average (ARIMA) and multilayer perceptrons (MLPs) are used to construct two series hybrid models, ARIMA-MLP and MLP-ARIMA, and three parallel hybrid models, simple average, linear regression, and genetic algorithm models.ConclusionThe empirical forecasting results for two benchmark datasets, that is, the closing of the Shenzhen Integrated Index (SZII) and that of Standard and Poor’s 500 (S&P 500), indicate that although all hybrid models perform better than at least one of their individual components, the series combination strategy produces more accurate hybrid models for financial time series forecasting.
Highlights
Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers
Given that the literature on time series forecasting remains ambiguous on the choice of combination strategy, the core objective of this study is to introduce an effective combination methodology and elucidate how individual models can be combined to improve financial time series forecasting
The dataset and modeling process for the hybrid models are presented in the subsections
Summary
Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. While it has been proven that these strategies can improve overall forecasting accuracy, the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model. Improving forecasting accuracy and introducing reliable forecasting methods can facilitate more profitable financial market investments by lead investors and financiers. To this effect, choosing a method that. To provide more accurate results, studies on time series forecasting and modeling widely use a combination of different models and metaheuristic optimization approaches. Recent studies on time series forecasting largely focus on combination methods given the distinguishing features of hybrid models (e.g., unique modeling capability of each model), drawbacks in using single models, and the resultant improvements in forecasting accuracy. Combining various models simplifies the selection of a model that is appropriate to process different forms of relationships in the data and reduces the risk of choosing an inefficient one
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