Correlation Between Namu Tech and Sungchang Autotech
Can any of the company-specific risk be diversified away by investing in both Namu Tech 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 Namu Tech and Sungchang Autotech into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Namu Tech CoLtd and Sungchang Autotech Co, you can compare the effects of market volatilities on Namu Tech 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 Namu Tech with a short position of Sungchang Autotech. Check out your portfolio center. Please also check ongoing floating volatility patterns of Namu Tech and Sungchang Autotech.
Diversification Opportunities for Namu Tech and Sungchang Autotech
0.57 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Namu and Sungchang is 0.57. Overlapping area represents the amount of risk that can be diversified away by holding Namu Tech CoLtd and Sungchang Autotech Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sungchang Autotech and Namu Tech 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 Namu Tech CoLtd 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 Namu Tech i.e., Namu Tech and Sungchang Autotech go up and down completely randomly.
Pair Corralation between Namu Tech and Sungchang Autotech
Assuming the 90 days trading horizon Namu Tech CoLtd is expected to under-perform the Sungchang Autotech. But the stock apears to be less risky and, when comparing its historical volatility, Namu Tech CoLtd is 2.41 times less risky than Sungchang Autotech. The stock trades about -0.51 of its potential returns per unit of risk. The Sungchang Autotech Co is currently generating about -0.01 of returns per unit of risk over similar time horizon. If you would invest 356,500 in Sungchang Autotech Co on December 9, 2024 and sell it today you would lose (3,500) from holding Sungchang Autotech Co or give up 0.98% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Namu Tech CoLtd vs. Sungchang Autotech Co
Performance |
Timeline |
Namu Tech CoLtd |
Sungchang Autotech |
Namu Tech and Sungchang Autotech Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Namu Tech and Sungchang Autotech
The main advantage of trading using opposite Namu Tech and Sungchang Autotech positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Namu Tech 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.Namu Tech vs. CU Tech Corp | Namu Tech vs. Woori Technology | Namu Tech vs. Intellian Technologies | Namu Tech vs. Ewon Comfortech 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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