Correlation Between Meta Platforms and Spark Networks
Can any of the company-specific risk be diversified away by investing in both Meta Platforms and Spark Networks 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 Meta Platforms and Spark Networks into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Meta Platforms and Spark Networks SE, you can compare the effects of market volatilities on Meta Platforms and Spark Networks 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 Meta Platforms with a short position of Spark Networks. Check out your portfolio center. Please also check ongoing floating volatility patterns of Meta Platforms and Spark Networks.
Diversification Opportunities for Meta Platforms and Spark Networks
0.29 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Meta and Spark is 0.29. Overlapping area represents the amount of risk that can be diversified away by holding Meta Platforms and Spark Networks SE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Spark Networks SE and Meta Platforms 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 Meta Platforms are associated (or correlated) with Spark Networks. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Spark Networks SE has no effect on the direction of Meta Platforms i.e., Meta Platforms and Spark Networks go up and down completely randomly.
Pair Corralation between Meta Platforms and Spark Networks
Allowing for the 90-day total investment horizon Meta Platforms is expected to generate 0.28 times more return on investment than Spark Networks. However, Meta Platforms is 3.52 times less risky than Spark Networks. It trades about -0.13 of its potential returns per unit of risk. Spark Networks SE is currently generating about -0.05 per unit of risk. If you would invest 20,377 in Meta Platforms on January 26, 2024 and sell it today you would lose (3,428) from holding Meta Platforms or give up 16.82% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 9.42% |
Values | Daily Returns |
Meta Platforms vs. Spark Networks SE
Performance |
Timeline |
Meta Platforms |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Spark Networks SE |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Meta Platforms and Spark Networks Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Meta Platforms and Spark Networks
The main advantage of trading using opposite Meta Platforms and Spark Networks positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Meta Platforms position performs unexpectedly, Spark Networks 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 Spark Networks will offset losses from the drop in Spark Networks' long position.Meta Platforms vs. Meta Platforms | Meta Platforms vs. Alphabet Inc Class A | Meta Platforms vs. Twilio Inc | Meta Platforms vs. Snap Inc |
Spark Networks vs. Locafy Limited | Spark Networks vs. Metalpha Technology Holding | Spark Networks vs. TuanChe ADR | Spark Networks vs. Thryv Holdings |
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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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