Applying artificial intelligence in index tracking : case of the FTSE 100 index

Abstract

The use of Artificial intelligence (AI) in index tracking has in recent years gained a lot of attention not only of researchers but also of the everyday person. The existing applications focus on sample replication, which refers to the purchase of a small number of assets to track a benchmark index. Implementing sample replication requires asset selection and asset weighting. This master thesis presents an analysis of an artificial intelligence based method in asset weighting and further comparing it to a classical method. The Ridge regression, representing AI, is applied to compute the weighting scheme of the tracking portfolio with budget and no-short selling constraints. The classical method is based on tracking error optimization model. The proposed models are empirically test on the real-world dataset, the FTSE 100 stock index with data ranging from 2009 to 2014. The study is carried out on five different time periods to analyze how the models react with different market phases. The tests show that the Ridge regression model can produce the tracking portfolios, which have tracking qualities with higher values and lower variance, regardless to different market conditions. However, the classical model proved to be more powerful in terms of predicting the relative positions of the tracking portfolios.