Modelling and prediction of stock price dynamics using system identification methodology based on a popularly used technique analysis data

This paper discusses the time series analysis, modelling and prediction of stock price based on the popularly used technique analysis data MACD, RSI, MFI and ATR. The idea is that the stock price dynamics is treated as an unknown stochastic dynamic system to be identified. The stock price is treated as the system output and the technique analysis data such as MACD, RSI, MFI and ATR are treated as the system inputs. By using system identification techniques, the Extended Least Squares (ELS) method is applied to identify the system parameters. The UK Lloyds TSB data are taken as an example to show the performance of the modelling and prediction results.