Correlation Between Netflix and Democratic Large
Can any of the company-specific risk be diversified away by investing in both Netflix and Democratic Large at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Netflix and Democratic Large into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Netflix and Democratic Large Cap, you can compare the effects of market volatilities on Netflix and Democratic Large and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Netflix with a short position of Democratic Large. Check out your portfolio center. Please also check ongoing floating volatility patterns of Netflix and Democratic Large.
Diversification Opportunities for Netflix and Democratic Large
0.8 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Netflix and Democratic is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding Netflix and Democratic Large Cap in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Democratic Large Cap and Netflix is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Netflix are associated (or correlated) with Democratic Large. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Democratic Large Cap has no effect on the direction of Netflix i.e., Netflix and Democratic Large go up and down completely randomly.
Pair Corralation between Netflix and Democratic Large
Given the investment horizon of 90 days Netflix is expected to generate 2.23 times more return on investment than Democratic Large. However, Netflix is 2.23 times more volatile than Democratic Large Cap. It trades about 0.16 of its potential returns per unit of risk. Democratic Large Cap is currently generating about 0.14 per unit of risk. If you would invest 45,989 in Netflix on September 5, 2024 and sell it today you would earn a total of 45,117 from holding Netflix or generate 98.1% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Netflix vs. Democratic Large Cap
Performance |
| Timeline |
| Netflix |
| Democratic Large Cap |
Netflix and Democratic Large Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Netflix and Democratic Large
The main advantage of trading using opposite Netflix and Democratic Large positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Netflix position performs unexpectedly, Democratic Large can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Democratic Large will offset losses from the drop in Democratic Large's long position.| Netflix vs. Paramount Global Class | Netflix vs. Roku Inc | Netflix vs. Warner Bros Discovery | Netflix vs. AMC Entertainment Holdings |
| Democratic Large vs. Vanguard Total Stock | Democratic Large vs. SPDR SP 500 | Democratic Large vs. iShares Core SP | Democratic Large vs. Vanguard Dividend Appreciation |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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