Correlation Between Dongnam Chemical and KG Eco
Can any of the company-specific risk be diversified away by investing in both Dongnam Chemical and KG Eco 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 Dongnam Chemical and KG Eco into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dongnam Chemical Co and KG Eco Technology, you can compare the effects of market volatilities on Dongnam Chemical and KG Eco 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 Dongnam Chemical with a short position of KG Eco. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dongnam Chemical and KG Eco.
Diversification Opportunities for Dongnam Chemical and KG Eco
0.57 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Dongnam and 151860 is 0.57. Overlapping area represents the amount of risk that can be diversified away by holding Dongnam Chemical Co and KG Eco Technology in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on KG Eco Technology and Dongnam Chemical 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 Dongnam Chemical Co are associated (or correlated) with KG Eco. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of KG Eco Technology has no effect on the direction of Dongnam Chemical i.e., Dongnam Chemical and KG Eco go up and down completely randomly.
Pair Corralation between Dongnam Chemical and KG Eco
Assuming the 90 days trading horizon Dongnam Chemical is expected to generate 1.31 times less return on investment than KG Eco. But when comparing it to its historical volatility, Dongnam Chemical Co is 1.38 times less risky than KG Eco. It trades about 0.39 of its potential returns per unit of risk. KG Eco Technology is currently generating about 0.37 of returns per unit of risk over similar time horizon. If you would invest 420,965 in KG Eco Technology on October 10, 2024 and sell it today you would earn a total of 88,035 from holding KG Eco Technology or generate 20.91% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Dongnam Chemical Co vs. KG Eco Technology
Performance |
| Timeline |
| Dongnam Chemical |
| KG Eco Technology |
Dongnam Chemical and KG Eco Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Dongnam Chemical and KG Eco
The main advantage of trading using opposite Dongnam Chemical and KG Eco positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dongnam Chemical position performs unexpectedly, KG Eco 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 KG Eco will offset losses from the drop in KG Eco's long position.| Dongnam Chemical vs. AptaBio Therapeutics | Dongnam Chemical vs. Daewoo SBI SPAC | Dongnam Chemical vs. Dream Security co | Dongnam Chemical vs. Microfriend |
| KG Eco vs. Jeju Air Co | KG Eco vs. Hwangkum Steel Technology | KG Eco vs. Korea Air Svc | KG Eco vs. Eagle Veterinary Technology |
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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