A measure of time series' predictability using genetic programming applied to stock returns

Based on the standard genetic programming (GP) paradigm, we introduce a new probability measure of time series' predictability. It is computed as a ratio of two fitness values from GP runs. One value belongs to a subject series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the boundaries of the measure are between zero and 100, where zero characterizes stochastic processes while 100 typifies predictable ones. To evaluate its performance, we first apply it to experimental data. It is then applied to eight Dow Jones stock returns. This measure may reduce model search space and produce more reliable forecast models.