Correlation Between Mercuries Associates and Shin Kong
Can any of the company-specific risk be diversified away by investing in both Mercuries Associates and Shin Kong 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 Mercuries Associates and Shin Kong into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mercuries Associates Holding and Shin Kong Financial, you can compare the effects of market volatilities on Mercuries Associates and Shin Kong 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 Mercuries Associates with a short position of Shin Kong. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mercuries Associates and Shin Kong.
Diversification Opportunities for Mercuries Associates and Shin Kong
0.8 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Mercuries and Shin is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding Mercuries Associates Holding and Shin Kong Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shin Kong Financial and Mercuries Associates 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 Mercuries Associates Holding are associated (or correlated) with Shin Kong. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shin Kong Financial has no effect on the direction of Mercuries Associates i.e., Mercuries Associates and Shin Kong go up and down completely randomly.
Pair Corralation between Mercuries Associates and Shin Kong
Assuming the 90 days trading horizon Mercuries Associates Holding is expected to under-perform the Shin Kong. In addition to that, Mercuries Associates is 3.9 times more volatile than Shin Kong Financial. It trades about -0.09 of its total potential returns per unit of risk. Shin Kong Financial is currently generating about -0.15 per unit of volatility. If you would invest 3,730 in Shin Kong Financial on September 12, 2024 and sell it today you would lose (160.00) from holding Shin Kong Financial or give up 4.29% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Mercuries Associates Holding vs. Shin Kong Financial
Performance |
Timeline |
Mercuries Associates |
Shin Kong Financial |
Mercuries Associates and Shin Kong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mercuries Associates and Shin Kong
The main advantage of trading using opposite Mercuries Associates and Shin Kong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mercuries Associates position performs unexpectedly, Shin Kong 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 Shin Kong will offset losses from the drop in Shin Kong's long position.Mercuries Associates vs. Central Reinsurance Corp | Mercuries Associates vs. Huaku Development Co | Mercuries Associates vs. Fubon Financial Holding | Mercuries Associates vs. Chailease Holding 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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