# 07-Assessment (non-exam) Brief MSIN0104 – IQF – CW1

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Prof. Fred Malherbe

Portfolio Selection

The goal of this coursework is to practice portfolio selection on real-world data, using Python.

In the accompanying spreadsheet (“IQF_CW1_data”), you will find the daily returns for the period 1 Dec 2022 to 31 Jul 2023, of the 30 stocks that make up the Dow Jones index. Use this data as a proxy for the joint distribution of future payoffs and assume a daily risk-free rate of 2 basis points (bps) – which corresponds to a 5% annual risk- free rate.

Step 0

This step is just a warm up. I want you to work on it individually as a preparation for the group work. You should do it by Wednesday 11 October at the latest, and you should share your code with your team mates before your first group meeting. All individual codes should be submitted as appendices to the submission. Students who don’t submit their code will be penalised.

1. Import the data in Python.
2. Compute expected returns, standard deviations, and Sharpe Ratios for each stock.
3. Rank these stocks according to their Sharpe ratio.

Step 1

1. Considerallpossibleportfoliosthatarecomposedof5stocksequallyweighted (i.e. all the possible portfolios of 5 stocks, each weighted at 20%). Among all the portfolios of this type, find the one with the highest Sharpe ratio.
2. Now, restrict yourself to portfolios that only include the 5 stocks you have identified in Step 5. above, but no longer restrict yourself to an equally weighted portfolio. Instead, find the weights (at the 10bp precision level) for these 5 stocks that maximise the portfolio Sharpe ratio.

Step 2

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