Equal weighted portfolios

When comparing different portfolios, you often want to consider performance versus a naive equally-weighted portfolio. If the portfolio doesn't outperform a simple equally weighted portfolio, you might want to consider another strategy, or simply opt for the naive approach if all else fails. You can expect equally-weighted portfolios to tend to outperform the market when the largest companies are doing poorly. This is because even tiny companies would have the same weight in your equally-weighted portfolio as Apple or Amazon, for example.

To make it easier for you to visualize the cumulative returns of portfolios, we defined the function cumulative_returns_plot() in your workspace.

This is a part of the course

“Introduction to Portfolio Risk Management in Python”

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Exercise instructions

Set numstocks equal to 9, which is the number of stocks in your portfolio.
Use np.repeat() to set portfolio_weights_ew equal to an array with an equal weights for each of the 9 stocks.
Use the .iloc accessor to select all rows and the first 9 columns when calculating the portfolio return.
Finally, review the plot of cumulative returns over time.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# How many stocks are in your portfolio?
numstocks = ____

# Create an array of equal weights across all assets
portfolio_weights_ew = ____

# Calculate the equally-weighted portfolio returns
StockReturns['Portfolio_EW'] = StockReturns.iloc[____, ____].mul(portfolio_weights_ew, axis=1).sum(axis=1)
cumulative_returns_plot(['Portfolio', 'Portfolio_EW'])

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