Risk factor based investing : case: MSCI risk factor indices
1University of Oulu, Oulu Business School, Department of Finance, Finance
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201601141032
|Publish Date:|| 2016-01-18
|Thesis type:||Master's thesis
The aim of this thesis is to study risk factor based investing and test how well MSCI constructs their risk factor based indices. Risk factor based investing has gained a lot of media exposure in the recent years and “Smart Beta” products are becoming more popular. Blackrock estimated that there are more than 700 exchange traded products available and they have over $ 529 billion in assets under management. Risk factor investing aims to harvest the risk premia associated with factors like size, momentum and value. I tested whether MSCI is able to provide higher Sharpe ratios for higher risk exposure indices and how much they deviated from the parent index of MSCI World. I used the Ledoit & Wolf bootstrap inference test to find out whether the Sharpe ratios of high exposure and high capacity indices differ from each other. Furthermore, I tested how well the Fama & French Three Factor-model with the addition of Carhart momentum factor could explain the returns of MSCI’s risk factor indices. I also constructed different risk factor portfolios using risk-parity methods to see whether it is possible to enhance the returns of risk factor indices by combining them. The main results and conclusions of this thesis were that risk factor investing can provide excess returns. These excess returns are readily available by investing in MSCI’s risk factor indices. Another key finding was that by utilizing risk-parity methods an investor can achieve excess returns over an equally weighted risk factor portfolio and over the MSCI’s own Diversified Mix index. Furthermore, even though MSCI is the world leader in index creation, their way of creating indices doesn’t seem to be very efficient and it would be beneficial to analyse other index providers, too. The data used in this thesis were gathered from “MSCI’s end of day index data search”. The data consists of six risk factor indices from developed countries. The price data ranged from November 1998 to August 2015. For the Ledoit & Wolf test I gathered four high capacity indices and four high exposure indices from the same time period. The proxies for academic factors were provided by Kenneth French on his website.
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