Correlation Between Samyang Foods and Lotte Non
Can any of the company-specific risk be diversified away by investing in both Samyang Foods and Lotte Non 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 Samyang Foods and Lotte Non into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Samyang Foods Co and Lotte Non Life Insurance, you can compare the effects of market volatilities on Samyang Foods and Lotte Non 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 Samyang Foods with a short position of Lotte Non. Check out your portfolio center. Please also check ongoing floating volatility patterns of Samyang Foods and Lotte Non.
Diversification Opportunities for Samyang Foods and Lotte Non
-0.41 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Samyang and Lotte is -0.41. Overlapping area represents the amount of risk that can be diversified away by holding Samyang Foods Co and Lotte Non Life Insurance in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lotte Non Life and Samyang Foods 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 Samyang Foods Co are associated (or correlated) with Lotte Non. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lotte Non Life has no effect on the direction of Samyang Foods i.e., Samyang Foods and Lotte Non go up and down completely randomly.
Pair Corralation between Samyang Foods and Lotte Non
Assuming the 90 days trading horizon Samyang Foods Co is expected to generate 1.35 times more return on investment than Lotte Non. However, Samyang Foods is 1.35 times more volatile than Lotte Non Life Insurance. It trades about 0.01 of its potential returns per unit of risk. Lotte Non Life Insurance is currently generating about -0.18 per unit of risk. If you would invest 52,000,000 in Samyang Foods Co on September 1, 2024 and sell it today you would lose (100,000) from holding Samyang Foods Co or give up 0.19% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Samyang Foods Co vs. Lotte Non Life Insurance
Performance |
| Timeline |
| Samyang Foods |
| Lotte Non Life |
Samyang Foods and Lotte Non Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Samyang Foods and Lotte Non
The main advantage of trading using opposite Samyang Foods and Lotte Non positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Samyang Foods position performs unexpectedly, Lotte Non 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 Lotte Non will offset losses from the drop in Lotte Non's long position.| Samyang Foods vs. AptaBio Therapeutics | Samyang Foods vs. Daewoo SBI SPAC | Samyang Foods vs. Dream Security co | Samyang Foods vs. Microfriend |
| Lotte Non vs. AptaBio Therapeutics | Lotte Non vs. Daewoo SBI SPAC | Lotte Non vs. Dream Security co | Lotte Non vs. Microfriend |
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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