Correlation Between Innometry and Ssangyong Materials
Can any of the company-specific risk be diversified away by investing in both Innometry and Ssangyong Materials 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 Innometry and Ssangyong Materials into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Innometry Co and Ssangyong Materials Corp, you can compare the effects of market volatilities on Innometry and Ssangyong Materials 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 Innometry with a short position of Ssangyong Materials. Check out your portfolio center. Please also check ongoing floating volatility patterns of Innometry and Ssangyong Materials.
Diversification Opportunities for Innometry and Ssangyong Materials
-0.55 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Innometry and Ssangyong is -0.55. Overlapping area represents the amount of risk that can be diversified away by holding Innometry Co and Ssangyong Materials Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ssangyong Materials Corp and Innometry 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 Innometry Co are associated (or correlated) with Ssangyong Materials. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ssangyong Materials Corp has no effect on the direction of Innometry i.e., Innometry and Ssangyong Materials go up and down completely randomly.
Pair Corralation between Innometry and Ssangyong Materials
Assuming the 90 days trading horizon Innometry Co is expected to under-perform the Ssangyong Materials. But the stock apears to be less risky and, when comparing its historical volatility, Innometry Co is 1.11 times less risky than Ssangyong Materials. The stock trades about -0.19 of its potential returns per unit of risk. The Ssangyong Materials Corp is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 218,500 in Ssangyong Materials Corp on September 3, 2024 and sell it today you would earn a total of 20,500 from holding Ssangyong Materials Corp or generate 9.38% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Innometry Co vs. Ssangyong Materials Corp
Performance |
Timeline |
Innometry |
Ssangyong Materials Corp |
Innometry and Ssangyong Materials Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Innometry and Ssangyong Materials
The main advantage of trading using opposite Innometry and Ssangyong Materials positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Innometry position performs unexpectedly, Ssangyong Materials 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 Ssangyong Materials will offset losses from the drop in Ssangyong Materials' long position.Innometry vs. Daejoo Electronic Materials | Innometry vs. Parksystems Corp | Innometry vs. BH Co | Innometry 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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