Forecasting Accuracy Comparison of Various Machine Learning and Statistical Models on Stock Market Price Movements

Researcher: Ruan Pretorius, University of the Witwatersrand, Johannesburg
Supervisors: Prof. Terence van Zyl, University of Johannesburg and Dr Farai Mlambo, University of the Witwatersrand, Johannesburg

Accurate financial time series forecasts can assist investors in gaining a competitive edge over other participants in capital markets No empirical conclusion existed on what the most accurate model(s) were for forecasting stock market price movements over different forecast horizons Limitations from previous studies were addressed in this study by compared the forecasting accuracy of 20 different models on 403 time series of stocks/indices These included machine learning ( statistical and benchmark models The naïve benchmark model outperformed all other models in this study for nearly all accuracy metrics and forecast horizons tested.