Correlation Between Samsung Electronics and BLUESCOPE STEEL
Can any of the company-specific risk be diversified away by investing in both Samsung Electronics and BLUESCOPE STEEL 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 Samsung Electronics and BLUESCOPE STEEL into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Samsung Electronics Co and BLUESCOPE STEEL, you can compare the effects of market volatilities on Samsung Electronics and BLUESCOPE STEEL 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 Samsung Electronics with a short position of BLUESCOPE STEEL. Check out your portfolio center. Please also check ongoing floating volatility patterns of Samsung Electronics and BLUESCOPE STEEL.
Diversification Opportunities for Samsung Electronics and BLUESCOPE STEEL
-0.5 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Samsung and BLUESCOPE is -0.5. Overlapping area represents the amount of risk that can be diversified away by holding Samsung Electronics Co and BLUESCOPE STEEL in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BLUESCOPE STEEL and Samsung 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 Samsung Electronics Co are associated (or correlated) with BLUESCOPE STEEL. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BLUESCOPE STEEL has no effect on the direction of Samsung Electronics i.e., Samsung Electronics and BLUESCOPE STEEL go up and down completely randomly.
Pair Corralation between Samsung Electronics and BLUESCOPE STEEL
Assuming the 90 days trading horizon Samsung Electronics Co is expected to under-perform the BLUESCOPE STEEL. In addition to that, Samsung Electronics is 1.0 times more volatile than BLUESCOPE STEEL. It trades about -0.19 of its total potential returns per unit of risk. BLUESCOPE STEEL is currently generating about 0.09 per unit of volatility. If you would invest 1,209 in BLUESCOPE STEEL on September 1, 2024 and sell it today you would earn a total of 141.00 from holding BLUESCOPE STEEL or generate 11.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Samsung Electronics Co vs. BLUESCOPE STEEL
Performance |
Timeline |
Samsung Electronics |
BLUESCOPE STEEL |
Samsung Electronics and BLUESCOPE STEEL Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Samsung Electronics and BLUESCOPE STEEL
The main advantage of trading using opposite Samsung Electronics and BLUESCOPE STEEL positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Samsung Electronics position performs unexpectedly, BLUESCOPE STEEL 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 BLUESCOPE STEEL will offset losses from the drop in BLUESCOPE STEEL's long position.Samsung Electronics vs. VIVA WINE GROUP | Samsung Electronics vs. Haverty Furniture Companies | Samsung Electronics vs. American Homes 4 | Samsung Electronics vs. Marie Brizard Wine |
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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 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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