Correlation Between Tae Kyung and Digital Power
Can any of the company-specific risk be diversified away by investing in both Tae Kyung and Digital Power 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 Tae Kyung and Digital Power into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Tae Kyung Chemical and Digital Power Communications, you can compare the effects of market volatilities on Tae Kyung and Digital Power 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 Tae Kyung with a short position of Digital Power. Check out your portfolio center. Please also check ongoing floating volatility patterns of Tae Kyung and Digital Power.
Diversification Opportunities for Tae Kyung and Digital Power
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Tae and Digital is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Tae Kyung Chemical and Digital Power Communications in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Digital Power Commun and Tae Kyung 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 Tae Kyung Chemical are associated (or correlated) with Digital Power. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Digital Power Commun has no effect on the direction of Tae Kyung i.e., Tae Kyung and Digital Power go up and down completely randomly.
Pair Corralation between Tae Kyung and Digital Power
Assuming the 90 days trading horizon Tae Kyung Chemical is expected to generate 0.7 times more return on investment than Digital Power. However, Tae Kyung Chemical is 1.43 times less risky than Digital Power. It trades about 0.01 of its potential returns per unit of risk. Digital Power Communications is currently generating about -0.04 per unit of risk. If you would invest 1,089,000 in Tae Kyung Chemical on August 25, 2024 and sell it today you would earn a total of 0.00 from holding Tae Kyung Chemical or generate 0.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Tae Kyung Chemical vs. Digital Power Communications
Performance |
| Timeline |
| Tae Kyung Chemical |
| Digital Power Commun |
Tae Kyung and Digital Power Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Tae Kyung and Digital Power
The main advantage of trading using opposite Tae Kyung and Digital Power positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Tae Kyung position performs unexpectedly, Digital Power 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 Digital Power will offset losses from the drop in Digital Power's long position.| Tae Kyung vs. Osang Healthcare Co,Ltd | Tae Kyung vs. Samsung Publishing Co | Tae Kyung vs. GS Retail Co | Tae Kyung vs. KakaoBank Corp |
| Digital Power vs. AptaBio Therapeutics | Digital Power vs. Daewoo SBI SPAC | Digital Power vs. Dream Security co | Digital Power 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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