Correlation Between DB Financial and Sungchang Autotech
Can any of the company-specific risk be diversified away by investing in both DB Financial and Sungchang Autotech 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 DB Financial and Sungchang Autotech into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DB Financial Investment and Sungchang Autotech Co, you can compare the effects of market volatilities on DB Financial and Sungchang Autotech 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 DB Financial with a short position of Sungchang Autotech. Check out your portfolio center. Please also check ongoing floating volatility patterns of DB Financial and Sungchang Autotech.
Diversification Opportunities for DB Financial and Sungchang Autotech
0.01 | Correlation Coefficient |
Significant diversification
The 3 months correlation between 016610 and Sungchang is 0.01. Overlapping area represents the amount of risk that can be diversified away by holding DB Financial Investment and Sungchang Autotech Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sungchang Autotech and DB Financial 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 DB Financial Investment are associated (or correlated) with Sungchang Autotech. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sungchang Autotech has no effect on the direction of DB Financial i.e., DB Financial and Sungchang Autotech go up and down completely randomly.
Pair Corralation between DB Financial and Sungchang Autotech
Assuming the 90 days trading horizon DB Financial Investment is expected to generate 1.02 times more return on investment than Sungchang Autotech. However, DB Financial is 1.02 times more volatile than Sungchang Autotech Co. It trades about 0.06 of its potential returns per unit of risk. Sungchang Autotech Co is currently generating about -0.05 per unit of risk. If you would invest 420,135 in DB Financial Investment on August 25, 2024 and sell it today you would earn a total of 89,865 from holding DB Financial Investment or generate 21.39% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
DB Financial Investment vs. Sungchang Autotech Co
Performance |
Timeline |
DB Financial Investment |
Sungchang Autotech |
DB Financial and Sungchang Autotech Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DB Financial and Sungchang Autotech
The main advantage of trading using opposite DB Financial and Sungchang Autotech positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DB Financial position performs unexpectedly, Sungchang Autotech 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 Sungchang Autotech will offset losses from the drop in Sungchang Autotech's long position.DB Financial vs. Korea New Network | DB Financial vs. Dong A Eltek | DB Financial vs. Dreamus Company | DB Financial vs. SK Bioscience Co |
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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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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