On autoregressive moving-average models as a tool of virtual stock-exchange: experimental investigation

Abstract

The objective of this work is to investigate experimentally the well-known autoregressive models as simplest algorithms simulating prediction processes of the stockholders using the historical stock rates only. The “virtual” stock exchange which applies these algorithms can help in testing various assumptions of investor behavior. To represent users that prefer linear utility functions, the autoregressive moving-average model (ARMA-ABS(p, q)), minimizing the absolute values of prediction errors is regarded, in addition to the traditional ARMA(p, q) model which minimize the least square errors. The results of two hundred actual financial time series and a hundred of virtual ones are discussed in short.

Keywords

• forecasting
• autoregressive models
• stock exchange
• time series