A  smart beta investment strategy to make risk more transparent : A quantitative study as a contribute to lowering the systemic risk, without sacrifice of return.

This study have focused on the creation of a smart beta investment strategy to make risks in terms of beta for individual assets more transparent, and to explore if the constructed portfolios risk in terms of standard deviation significantly gets lower than for different benchmark indexes. The strategy could be used for investors who want to decrease their contribution to systemic risk, without sacrificing return. Further it has been investigated if the returns for the portfolio are significantly higher than for the benchmarks, and if there exists a correlation between the portfolio’s standard deviation and its return. The portfolio were constructed and followed during the period 2013-12-30 - 2017-01-05. The study had its starting point in criticism against EMH, CAPM and the way beta are commonly used. The study is a quantitative and deductive study, made from a positivistic point of view, where the approach and used data further defines it as experimental, longitudinal and retrospective. In the study, secondary data consisting of approximately123:000 numerical values of daily quoted prices for stocks and market indexeshave been used, which through different calculations generated primary data. The writer created five sub-indexes constituting of 16 stocks each, where the constutitents were based on different industries that can be observed on the Swedish stock exchange Stockholmsbörsen. For each stock in the sub-indexes, the beta (smart beta) was calculated at three points in time based on three years daily stock data, values which were the basis for which stocks to include and exclude in the portfolio. The included stocks at initial purchase and re-balancing dates were consequently based on having an even spread of smart betas, covering low to high values. All the sub-indexes and the portfolio were equally weighted, where the portfolio where re-balanced annually and some stocks were replaced. The results from the approach were a portfolio with lower standard deviation and considerably higher returns than for any of the benchmarks which represented the market and with good evaluation measures (SR and IR). The portfolio got a statistically significantly lower standard deviation than the benchmark indexes, whereas there were no significant correlation between the portfolios standard deviation and return. The returns of the portfolio could also not be proven to be statistical significantly higher than for the benchmarks. Finally, the majority of data turned out to not be normally distributed, making all statistical results questionable. Despite this, the writer believes that the results should be enough to motivate individuals to implement similar studies, creating a wider basis of risk transparent investment strategies which can decrease systemic risks