Correlation Between PNC Technologies and Sungchang Autotech
Can any of the company-specific risk be diversified away by investing in both PNC Technologies 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 PNC Technologies and Sungchang Autotech into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between PNC Technologies co and Sungchang Autotech Co, you can compare the effects of market volatilities on PNC Technologies 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 PNC Technologies with a short position of Sungchang Autotech. Check out your portfolio center. Please also check ongoing floating volatility patterns of PNC Technologies and Sungchang Autotech.
Diversification Opportunities for PNC Technologies and Sungchang Autotech
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between PNC and Sungchang is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding PNC Technologies co and Sungchang Autotech Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sungchang Autotech and PNC Technologies 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 PNC Technologies co 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 PNC Technologies i.e., PNC Technologies and Sungchang Autotech go up and down completely randomly.
Pair Corralation between PNC Technologies and Sungchang Autotech
Assuming the 90 days trading horizon PNC Technologies co is expected to generate 1.43 times more return on investment than Sungchang Autotech. However, PNC Technologies is 1.43 times more volatile than Sungchang Autotech Co. It trades about 0.02 of its potential returns per unit of risk. Sungchang Autotech Co is currently generating about -0.09 per unit of risk. If you would invest 405,500 in PNC Technologies co on September 22, 2024 and sell it today you would earn a total of 2,500 from holding PNC Technologies co or generate 0.62% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
PNC Technologies co vs. Sungchang Autotech Co
Performance |
Timeline |
PNC Technologies |
Sungchang Autotech |
PNC Technologies and Sungchang Autotech Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with PNC Technologies and Sungchang Autotech
The main advantage of trading using opposite PNC Technologies and Sungchang Autotech positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if PNC Technologies 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.PNC Technologies vs. Daejoo Electronic Materials | PNC Technologies vs. Parksystems Corp | PNC Technologies vs. BH Co | PNC Technologies vs. Partron 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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