Market-cap weighted portfolios
Conversely, when large companies are doing well, market capitalization, or "market cap" weighted portfolios tend to outperform. This is because the largest weights are being assigned to the largest companies, or the companies with the largest market cap.
Below is a table of the market capitalizations of the companies in your portfolio just before January 2017:
| Company Name | Ticker | Market Cap ($ Billions) |
|---|---|---|
| Apple | AAPL | 601.51 |
| Microsoft | MSFT | 469.25 |
| Exxon Mobil | XOM | 349.5 |
| Johnson & Johnson | JNJ | 310.48 |
| JP Morgan | JPM | 299.77 |
| Amazon | AMZN | 356.94 |
| General Electric | GE | 268.88 |
| FB | 331.57 | |
| AT&T | T | 246.09 |
Diese Übung ist Teil des Kurses
Introduction to Portfolio Risk Management in Python
Anleitung zur Übung
- Finish defining the
market_capitalizationsarray of market capitalizations in billions according to the table above. - Calculate
mcap_weightsarray such that each element is the ratio of market cap of the company to the total market cap of all companies. - Use the
.mul()method on themcap_weightsand returns to calculate the market capitalization weighted portfolio returns. - Finally, review the plot of cumulative returns over time.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Create an array of market capitalizations (in billions)
market_capitalizations = np.array([601.51, 469.25, 349.5, 310.48, 299.77, 356.94, 268.88, 331.57, ____])
# Calculate the market cap weights
mcap_weights = ____
# Calculate the market cap weighted portfolio returns
StockReturns['Portfolio_MCap'] = StockReturns.iloc[:, 0:9].mul(____, axis=1).sum(axis=1)
cumulative_returns_plot(['Portfolio', 'Portfolio_EW', 'Portfolio_MCap'])