Forecast based portfolio optimisation using XGBoost - May, Khanya (My University, 2022)
Portfolio optimisation is a vital research field in modern finance. In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of portfolio optimisation. In this research, it is demonstrated how using a new methodology that involves using XGBoost regressor chains to forecast stock prices, then incorporating these prices in k-means algorithm, selecting the assets with the highest Sharpe ratio in each cluster then allocating weights to the assets using Monte Carlo simulations. Historical stock price data of the assets in the JSE top 40 index is used. The performance of the model is evaluated using 2 test periods, 2019 as the non-crisis test period and 2020 for the crisis stress test period. The optimal portfolio has the best performance in both periods earning 94.73% returns with a Sharpe ratio of 0.1999 in 2019 and 11.02% returns with a Sharpe ratio of 0.029 in 2020.
A dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, Johannesburg, 2022
Keywords: Portfolio optimisation; XGBoost regressor chains; Forecast Based Portfolio