Correlation Between UMC Electronics and BURLINGTON STORES
Can any of the company-specific risk be diversified away by investing in both UMC Electronics and BURLINGTON STORES 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 UMC Electronics and BURLINGTON STORES into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between UMC Electronics Co and BURLINGTON STORES, you can compare the effects of market volatilities on UMC Electronics and BURLINGTON STORES 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 UMC Electronics with a short position of BURLINGTON STORES. Check out your portfolio center. Please also check ongoing floating volatility patterns of UMC Electronics and BURLINGTON STORES.
Diversification Opportunities for UMC Electronics and BURLINGTON STORES
-0.89 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between UMC and BURLINGTON is -0.89. Overlapping area represents the amount of risk that can be diversified away by holding UMC Electronics Co and BURLINGTON STORES in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BURLINGTON STORES and UMC Electronics 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 UMC Electronics Co are associated (or correlated) with BURLINGTON STORES. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BURLINGTON STORES has no effect on the direction of UMC Electronics i.e., UMC Electronics and BURLINGTON STORES go up and down completely randomly.
Pair Corralation between UMC Electronics and BURLINGTON STORES
Assuming the 90 days horizon UMC Electronics Co is expected to generate 2.16 times more return on investment than BURLINGTON STORES. However, UMC Electronics is 2.16 times more volatile than BURLINGTON STORES. It trades about 0.14 of its potential returns per unit of risk. BURLINGTON STORES is currently generating about 0.1 per unit of risk. If you would invest 168.00 in UMC Electronics Co on October 23, 2024 and sell it today you would earn a total of 11.00 from holding UMC Electronics Co or generate 6.55% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
UMC Electronics Co vs. BURLINGTON STORES
Performance |
Timeline |
UMC Electronics |
BURLINGTON STORES |
UMC Electronics and BURLINGTON STORES Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with UMC Electronics and BURLINGTON STORES
The main advantage of trading using opposite UMC Electronics and BURLINGTON STORES positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if UMC Electronics position performs unexpectedly, BURLINGTON STORES 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 BURLINGTON STORES will offset losses from the drop in BURLINGTON STORES's long position.UMC Electronics vs. Amphenol | UMC Electronics vs. Hon Hai Precision | UMC Electronics vs. Samsung SDI Co | UMC Electronics vs. Murata Manufacturing Co |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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