Predicting the Stock Market using Multiple Models

Stock market prediction has always been, in the past and at present, an intriguing issue. In this paper, an attempt is made at predicting the Standard & Poor´s (S&P) 500 returns on a daily and weekly basis by using only historical price data. Two different types of prediction models are used for the prediction task: the auto-regressive (AR) and the neural network (NN) models. These two models are used in four different prediction systems. The first two prediction systems consist of either an AR model or a NN model. The next two prediction systems represent the novelty of the approach used in this paper. A multiple-model approach is proposed, together with the use of a trend classification algorithm, to predict the S&P 500 returns. Three models (either AR or NN) are used in each of the systems, with each model used to represent one of the three market trends (bear, choppy and bull). A decision rule is used to select one prediction from the three models, and one of two trading rules is used to make trading decisions. Three experiments were carried out to select appropriate parameters for the three-model systems. Evaluation of the models based on ARR after commission showed that the system consisting of three NNs was able to obtain approximately two times as much return as the buy-and-hold strategy in the test period when used in weekly predictions. Furthermore, the results in this paper show that non-linear systems performed better than linear ones, and three-model systems performed better than single-model ones