Correlation Between Kang Yong and Steel Public
Can any of the company-specific risk be diversified away by investing in both Kang Yong and Steel Public 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 Kang Yong and Steel Public into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kang Yong Electric and The Steel Public, you can compare the effects of market volatilities on Kang Yong and Steel Public 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 Kang Yong with a short position of Steel Public. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kang Yong and Steel Public.
Diversification Opportunities for Kang Yong and Steel Public
0.62 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Kang and Steel is 0.62. Overlapping area represents the amount of risk that can be diversified away by holding Kang Yong Electric and The Steel Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Steel Public and Kang Yong 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 Kang Yong Electric are associated (or correlated) with Steel Public. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Steel Public has no effect on the direction of Kang Yong i.e., Kang Yong and Steel Public go up and down completely randomly.
Pair Corralation between Kang Yong and Steel Public
Assuming the 90 days trading horizon Kang Yong is expected to generate 116.93 times less return on investment than Steel Public. But when comparing it to its historical volatility, Kang Yong Electric is 3.85 times less risky than Steel Public. It trades about 0.0 of its potential returns per unit of risk. The Steel Public is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest 72.00 in The Steel Public on October 5, 2024 and sell it today you would earn a total of 2.00 from holding The Steel Public or generate 2.78% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Kang Yong Electric vs. The Steel Public
Performance |
Timeline |
Kang Yong Electric |
Steel Public |
Kang Yong and Steel Public Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Kang Yong and Steel Public
The main advantage of trading using opposite Kang Yong and Steel Public positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kang Yong position performs unexpectedly, Steel Public 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 Steel Public will offset losses from the drop in Steel Public's long position.Kang Yong vs. Hwa Fong Rubber | Kang Yong vs. Hana Microelectronics Public | Kang Yong vs. KGI Securities Public | Kang Yong vs. Haad Thip Public |
Steel Public vs. Land and Houses | Steel Public vs. CH Karnchang Public | Steel Public vs. Krung Thai Bank | Steel Public vs. Bangkok Bank Public |
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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