Correlation Between Vinhomes JSC and FPT Digital
Can any of the company-specific risk be diversified away by investing in both Vinhomes JSC and FPT Digital 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 Vinhomes JSC and FPT Digital into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Vinhomes JSC and FPT Digital Retail, you can compare the effects of market volatilities on Vinhomes JSC and FPT Digital 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 Vinhomes JSC with a short position of FPT Digital. Check out your portfolio center. Please also check ongoing floating volatility patterns of Vinhomes JSC and FPT Digital.
Diversification Opportunities for Vinhomes JSC and FPT Digital
0.34 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Vinhomes and FPT is 0.34. Overlapping area represents the amount of risk that can be diversified away by holding Vinhomes JSC and FPT Digital Retail in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FPT Digital Retail and Vinhomes JSC 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 Vinhomes JSC are associated (or correlated) with FPT Digital. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FPT Digital Retail has no effect on the direction of Vinhomes JSC i.e., Vinhomes JSC and FPT Digital go up and down completely randomly.
Pair Corralation between Vinhomes JSC and FPT Digital
Assuming the 90 days trading horizon Vinhomes JSC is expected to generate 9.9 times less return on investment than FPT Digital. But when comparing it to its historical volatility, Vinhomes JSC is 1.02 times less risky than FPT Digital. It trades about 0.01 of its potential returns per unit of risk. FPT Digital Retail is currently generating about 0.15 of returns per unit of risk over similar time horizon. If you would invest 10,110,000 in FPT Digital Retail on September 3, 2024 and sell it today you would earn a total of 7,870,000 from holding FPT Digital Retail or generate 77.84% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Vinhomes JSC vs. FPT Digital Retail
Performance |
| Timeline |
| Vinhomes JSC |
| FPT Digital Retail |
Vinhomes JSC and FPT Digital Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Vinhomes JSC and FPT Digital
The main advantage of trading using opposite Vinhomes JSC and FPT Digital positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Vinhomes JSC position performs unexpectedly, FPT Digital 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 FPT Digital will offset losses from the drop in FPT Digital's long position.| Vinhomes JSC vs. Innovative Technology Development | Vinhomes JSC vs. Tien Giang Investment | Vinhomes JSC vs. Hai An Transport | Vinhomes JSC vs. Ha Long Investment |
| FPT Digital vs. Fecon Mining JSC | FPT Digital vs. Vincom Retail JSC | FPT Digital vs. POST TELECOMMU | FPT Digital vs. VTC Telecommunications JSC |
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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