Asset allocation of the Norwegian Government Pension Fund Global with programming in Python
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- Master of Science 
This thesis aims to assess if the optimal asset allocation for the Norwegian Government Pension Fund – Global could be improved. We were curious to see if we were able to optimize the portfolio by only looking at the risk-return relationship, without taking political, economic or ethical interest into evaluation. Since Norges Bank Investment Management has expressed that they do not have the absolute answer for what is the optimal asset allocation, we were interested to research whether or not we could obtain better results by purely examine the financial performance. The research set out to calculate the optimal portfolio weighting from historical data collected from 2008 until 2018. To analyze and compare the results we used the FTSE Benchmark Index, which is the benchmark used for the Government Pension Fund – Global. Therefore, we tried to replicate the FTSE benchmark by using 25 of the same countries in our portfolio. Upon advice from our supervisor we chose to program our own portfolio optimizer from scratch with Python as our programming tool. We constructed the different portfolios and divided them into constant expected return and time-varying expected return. Even though this was much more time consuming than using another software, we found it to be rewarding. As expected, our results showed that with our asset allocation we did not outperform the benchmark, except one portfolio that is rather close. However, this portfolio was closer than one should assume – compared to the benchmark Norges Bank uses that has considerably more complexity and more political-economic decisions behind its investment strategy. So – is it possible to rather concentrate on the return and variance tradeoff instead off introducing the vast complexity of several influencing factors.
Masteroppgave(MSc) in Master of Science in Business, Economics/Masteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2018