Evolving Stock Market Prediction Models Using Soft Computing Techniques

Abstract

Stock market prediction is one of the hottest field of research lately due to its business applications owing to high stakes and the kinds of attractive benefits that it has to offer. The stock market is a dynamic, non-linear, complex, and chaotic in nature, forecasting stock market price is an important financial problem that is receiving increasing attention. During the last few years, a number of many models were presented to develop a relationship between the attributes which affect the stock index and its values. This thesis proposes a soft computing technique enhanced decision in financial management. The decision allows investors to maximize their expected return while practicing the prediction against financial risks. The importance of the research stems from the fact that it can be used to reduce the risk associated with uncertainty price movements in the stock market. The literature review shows that there are a large number of studies trying to forecast movements in the stock market, but there is a lack of literature trying to improve stock market risk management strategies with soft computing techniques. This thesis addresses this gap by applying the existing body of literature in stock index forecasting with soft computing techniques to the domain of forecasting index movements. In particular, it analyses whether there is an influence features of stocks used to predict movements of the stock index can improve forecasting the stock index movement off an investor faces use S&P 500 dataset and data related to it to create new dataset has impact in the price; The S&P 500, or the Standard & Poor’s 500, is an American stock market index. The S&P 500 presented its first stock index in the year 1923. S&P 500 index found to have 27 influence features which affect the index values. A new market forecasting model based on soft computing and especially genetic programming is developed to enhance the investor decision. The model compare with traditional model Auto regression model and iv Artificial Neural Network model. The system analysis stock market and futures data and makes a prediction about expected stock market conditions one day and next week. Selected new Features dataset by used GA to decrease the complexity. We were expecting that not all these features are significant in computing the index. Thus, we split our work to two phases. The first phase is to develop models based on these 27 features using Multiple Linear Regression (MLR) and ANN. Not only that, but we also explored a promising technique, multigene symbolic regression Genetic Programming to provide a mathematical nonlinear relationship between these attributes. GP found to be a powerful algorithm for providing mathematical modeling. In the second phase of this thesis, we adopted GAs as a mechanism to select the best features that contribute to the modeling process. The set of best features are once again used to build S & P 500 prediction models. Although the developed models in the second phase are with slightly less performance than in the first phase but the models are much simplex in complexity. This suggests that the stock market can be forecast using soft computing technique. Overall, this thesis concludes that the proposed model achieves a significant improvement in the prediction of stock market index.