Asset allocation decision is ranked as the most important investment decision an investor should make. Researchers have developed many optimization tools to find the best allocation for investors. Our paper will focus on implementing Black-Litterman model together with resampling techniques for portfolio allocations. In our paper, we are going to empirically test the usefulness of those techniques. The results from our research proved that Black-Litterman model and Resampling techniques are advanced methods, which help to generate better allocations than the traditional Markowitz method does. As focusing on typical Canadian investors, our reference portfolio is consisted of S&P TSX, S&P 500, DEX Universe Bond Index, T-Bills and various Canadian hedge funds indices. Using new data sets, we will test whether the results presented in Kooli and Selam’s 2010 paper will still hold. Lastly, further thoughts of our research will be discussed.
FRM Project-Simon Fraser University
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