Prediction of stock movement using phase space reconstruction and extreme learning machines

ABSTRACTStock movement prediction is regarded as one of the most difficult, meaningful, and attractive research issues in the field of financial markets. The stock price data have non-stationary, noisy, and non-linear characteristics which make the movement and its prediction a challenging task. In this paper, we propose a framework to predict the stock price movement using phase space reconstruction (PSR) and extreme learning machines (ELM). The uniqueness of the framework is reflected by its feature transformation technique which computes the information distance from the transformed features in phase space. The distance from phase space dimensions are modelled with ELM to predict the stock price movement. A decision-level fusion is performed on the ELM models trained using each category of features to improve the prediction performance. The framework has been validated on one of the challenging Borsa Istanbul (BIST 100) dataset which is a widely used dataset in stock price prediction studies. The resul...