Correlation Between Opera and Weibo Corp
Can any of the company-specific risk be diversified away by investing in both Opera and Weibo Corp 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 Opera and Weibo Corp into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Opera and Weibo Corp, you can compare the effects of market volatilities on Opera and Weibo Corp 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 Opera with a short position of Weibo Corp. Check out your portfolio center. Please also check ongoing floating volatility patterns of Opera and Weibo Corp.
Diversification Opportunities for Opera and Weibo Corp
0.38 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Opera and Weibo is 0.38. Overlapping area represents the amount of risk that can be diversified away by holding Opera and Weibo Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Weibo Corp and Opera 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 Opera are associated (or correlated) with Weibo Corp. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Weibo Corp has no effect on the direction of Opera i.e., Opera and Weibo Corp go up and down completely randomly.
Pair Corralation between Opera and Weibo Corp
Given the investment horizon of 90 days Opera is expected to generate 0.93 times more return on investment than Weibo Corp. However, Opera is 1.07 times less risky than Weibo Corp. It trades about -0.09 of its potential returns per unit of risk. Weibo Corp is currently generating about -0.13 per unit of risk. If you would invest 1,891 in Opera on October 26, 2024 and sell it today you would lose (64.00) from holding Opera or give up 3.38% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Opera vs. Weibo Corp
Performance |
| Timeline |
| Opera |
| Weibo Corp |
Opera and Weibo Corp Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Opera and Weibo Corp
The main advantage of trading using opposite Opera and Weibo Corp positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Opera position performs unexpectedly, Weibo Corp 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 Weibo Corp will offset losses from the drop in Weibo Corp's long position.The idea behind Opera and Weibo Corp pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| Weibo Corp vs. YY Inc Class | Weibo Corp vs. DouYu International Holdings | Weibo Corp vs. Tencent Music Entertainment | Weibo Corp vs. Autohome |
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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