Actively managed funds have long been accused of being both expensive and unable to continuously outperform simple cheap passive index funds after fees has been paid. This study investigates whether a happy medium exists, bene ting from the best of the both worlds. A data-driven generic framework for smart beta investing is proposed and tested on exchange traded funds. An asset ltration approach is used to screen data, feature selection with agglomerative hierarchical clustering and a selection criteria ensures diversi cation and selection of instruments with desired properties. Conditional value-at-risk optimization with moment matching for scenario generation is used for asset allocation. A bettingagainst-beta factor, which is long leveraged low-beta ETFs and short high-betas ETFs yields positive riskadjusted returns. This study extends previous work by presenting an end to end framework considering funding and transaction cost applied to exchange traded funds. Hierarchical clustering is used to de ne the market portfolio and the evidence of a betting-against-beta factor is tested on index level.
|Educations||Graduate Diploma in Finance, (Diploma Programme) Final Thesis|
|Number of pages||57|
|Supervisors||Kourosh Marjani Rasmussen|