Correlation Between Kang Yong and Laguna Resorts
Can any of the company-specific risk be diversified away by investing in both Kang Yong and Laguna Resorts 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 Laguna Resorts 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 Laguna Resorts Hotels, you can compare the effects of market volatilities on Kang Yong and Laguna Resorts 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 Laguna Resorts. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kang Yong and Laguna Resorts.
Diversification Opportunities for Kang Yong and Laguna Resorts
0.99 | Correlation Coefficient |
No risk reduction
The 3 months correlation between Kang and Laguna is 0.99. Overlapping area represents the amount of risk that can be diversified away by holding Kang Yong Electric and Laguna Resorts Hotels in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Laguna Resorts Hotels 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 Laguna Resorts. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Laguna Resorts Hotels has no effect on the direction of Kang Yong i.e., Kang Yong and Laguna Resorts go up and down completely randomly.
Pair Corralation between Kang Yong and Laguna Resorts
Assuming the 90 days trading horizon Kang Yong Electric is expected to generate 1.0 times more return on investment than Laguna Resorts. However, Kang Yong Electric is 1.0 times less risky than Laguna Resorts. It trades about 0.08 of its potential returns per unit of risk. Laguna Resorts Hotels is currently generating about 0.08 per unit of risk. If you would invest 30,339 in Kang Yong Electric on September 2, 2024 and sell it today you would lose (1,639) from holding Kang Yong Electric or give up 5.4% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Kang Yong Electric vs. Laguna Resorts Hotels
Performance |
| Timeline |
| Kang Yong Electric |
| Laguna Resorts Hotels |
Kang Yong and Laguna Resorts Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Kang Yong and Laguna Resorts
The main advantage of trading using opposite Kang Yong and Laguna Resorts positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kang Yong position performs unexpectedly, Laguna Resorts 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 Laguna Resorts will offset losses from the drop in Laguna Resorts' 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 |
| Laguna Resorts vs. TRC Construction Public | Laguna Resorts vs. Bangkok Expressway and | Laguna Resorts vs. Lohakit Metal Public | Laguna Resorts vs. Gunkul Engineering 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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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