Correlation Between Seoul Electronics and LAKE MATERIALS
Can any of the company-specific risk be diversified away by investing in both Seoul Electronics and LAKE MATERIALS 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 Seoul Electronics and LAKE MATERIALS into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Seoul Electronics Telecom and LAKE MATERIALS LTD, you can compare the effects of market volatilities on Seoul Electronics and LAKE MATERIALS 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 Seoul Electronics with a short position of LAKE MATERIALS. Check out your portfolio center. Please also check ongoing floating volatility patterns of Seoul Electronics and LAKE MATERIALS.
Diversification Opportunities for Seoul Electronics and LAKE MATERIALS
0.74 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Seoul and LAKE is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding Seoul Electronics Telecom and LAKE MATERIALS LTD in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on LAKE MATERIALS LTD and Seoul 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 Seoul Electronics Telecom are associated (or correlated) with LAKE MATERIALS. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of LAKE MATERIALS LTD has no effect on the direction of Seoul Electronics i.e., Seoul Electronics and LAKE MATERIALS go up and down completely randomly.
Pair Corralation between Seoul Electronics and LAKE MATERIALS
Assuming the 90 days trading horizon Seoul Electronics Telecom is expected to generate 0.41 times more return on investment than LAKE MATERIALS. However, Seoul Electronics Telecom is 2.44 times less risky than LAKE MATERIALS. It trades about -0.09 of its potential returns per unit of risk. LAKE MATERIALS LTD is currently generating about -0.15 per unit of risk. If you would invest 26,100 in Seoul Electronics Telecom on August 29, 2024 and sell it today you would lose (800.00) from holding Seoul Electronics Telecom or give up 3.07% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Seoul Electronics Telecom vs. LAKE MATERIALS LTD
Performance |
Timeline |
Seoul Electronics Telecom |
LAKE MATERIALS LTD |
Seoul Electronics and LAKE MATERIALS Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Seoul Electronics and LAKE MATERIALS
The main advantage of trading using opposite Seoul Electronics and LAKE MATERIALS positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Seoul Electronics position performs unexpectedly, LAKE MATERIALS 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 LAKE MATERIALS will offset losses from the drop in LAKE MATERIALS's long position.Seoul Electronics vs. Samsung Electronics Co | Seoul Electronics vs. Samsung Electronics Co | Seoul Electronics vs. KB Financial Group | Seoul Electronics vs. Shinhan Financial Group |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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