Prediction of market prices is an important and well-researched problem. While traditional techniques have yielded good results, rooms for improvement still exists, especially in the ability to explain sudden changes in behavior, as a response to shocks. Nonlinear systems have been successfully used to describe phase transitions in deterministic chaotic systems, so the combination of the expressive power of nonlinear systems and the efficient computation of linear models is an attractive idea. On such basis, in this work, an hybrid model is proposed that tunes its regression parameters with the results of nonlinear tools. Experiments, performed on several stocks in diverse sector and markets, show interesting performances, confirming as well the presence of distinct phases in the stock evolution, characterized by distinctly separated dynamics.
Stock price forecasting with an hybrid model
Ugo Fiore
2017-01-01
Abstract
Prediction of market prices is an important and well-researched problem. While traditional techniques have yielded good results, rooms for improvement still exists, especially in the ability to explain sudden changes in behavior, as a response to shocks. Nonlinear systems have been successfully used to describe phase transitions in deterministic chaotic systems, so the combination of the expressive power of nonlinear systems and the efficient computation of linear models is an attractive idea. On such basis, in this work, an hybrid model is proposed that tunes its regression parameters with the results of nonlinear tools. Experiments, performed on several stocks in diverse sector and markets, show interesting performances, confirming as well the presence of distinct phases in the stock evolution, characterized by distinctly separated dynamics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
