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Abstract

This Master Thesis introduces portfolio selection trading strategy named ”Threshold Based Online Algorithm”. A decision into which assets to invest is based on the thresh- old calculated from previous trading periods. In this work have been proposed two different ways for calculating the threshold. The main idea of the algorithm is to exploit the mean reversion property of stock markets by identifying assets that are expected to increase(decrease) in the following trading periods. We run numerical experiments on real datasets to estimate the algorithm performance efficiency. By analysing the empirical performance results, we figured out that TBOA trade-off between wealth per- formance, volatility and downside risks. It performed better on portfolio that contains highly volatile assets with low correlation between them. Evaluation results have shown, that TBOA was able to outperform already existing algorithms on some real datasets. Moreover TBOA has linear time complexity that makes algorithm runs fast.