Time interval choices in forecasting stock market indices of CEE and SEE countries

ABSTRACT

The main objective of this analysis is to investigate how varying the forecast horizon and the input window length for calculating technical indicators affects the predictive performance of different machine learning algorithms on forecasting the direction of change of chosen stock market indices. Ten indices from CEE (Central and Eastern European) and SEE (Southern and Eastern European) countries are chosen for research in an attempt to investigate their behaviour in the light of the behaviour of bigger and more researched markets. In respect to similar research conducted on S&P 500 Index stocks, this analysis does not find the same pattern of highest system performance for each forecast horizon value when the input window length is approximately equal to the forecasting horizon. Instead, the forecasts seem to be better using shorter input window lengths for technical indicators in general. Also, on average, there is a notable deterioration in the performance with the increase of forecasting horizon. Furthermore, some algorithms perform very well for short horizons and then deteriorate substantially as the forecasting horizon increases, while others seem to have more consistent performance over different horizons.

KEYWORDS:

• Technical trading
• stock price prediction
• financial forecasting
• classification algorithms
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author.