Towards building a hybrid model for predicting stock indexes

摘要

Predicting stock prices using computer generated models has been a popular research topic and has also been widely explored. However, the connectivity of the global financial market, availability of big data in multiple domains that influence the financial market, accessibility of information in real time and the demand for fast analytics continue to offer new research challenges. One of the complexities stems from the numerous ways in which we seek to set prediction parameters, whether it is the difference in an individual stocks' growth pattern or the time frame in which the predictions occur. The level of complexity has created a trend towards more advanced techniques in this field namely, the research into developing hybrid models that are composed of multiple prediction models with a view to yield more accurate results. The Proposed Hybrid Model (PHM) used in this paper is a combination of an Exponential Smoothing Model (ESM), an Auto Regressive Integrated Moving Average (ARIMA) model, and a Back-propagation Neural Network (BPNN) model. PHM combines the predictions of each of the component model based on weights assigned by a genetic algorithm, which is designed to provide an optimum output. In this paper, we seek to use the S&P 400 and 500 indexes to train and test the PHM to find daily closing values. For comparison of the results, Directional Accuracy (DA) is used as a metric. It was found that the results for the ARIMA and ESM on daily stock index data were far less accurate than that of the BPNN, which received comparable results to the baseline. However, due to the poor results of the ARIMA and ESM the hybrid model showed no significant results for the data and was inferior to the baseline.