Correlation Between Reynolds Consumer and TriMas
Can any of the company-specific risk be diversified away by investing in both Reynolds Consumer and TriMas 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 Reynolds Consumer and TriMas into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Reynolds Consumer Products and TriMas, you can compare the effects of market volatilities on Reynolds Consumer and TriMas 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 Reynolds Consumer with a short position of TriMas. Check out your portfolio center. Please also check ongoing floating volatility patterns of Reynolds Consumer and TriMas.
Diversification Opportunities for Reynolds Consumer and TriMas
-0.04 | Correlation Coefficient |
Good diversification
The 3 months correlation between Reynolds and TriMas is -0.04. Overlapping area represents the amount of risk that can be diversified away by holding Reynolds Consumer Products and TriMas in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on TriMas and Reynolds Consumer 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 Reynolds Consumer Products are associated (or correlated) with TriMas. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of TriMas has no effect on the direction of Reynolds Consumer i.e., Reynolds Consumer and TriMas go up and down completely randomly.
Pair Corralation between Reynolds Consumer and TriMas
Given the investment horizon of 90 days Reynolds Consumer Products is expected to under-perform the TriMas. But the stock apears to be less risky and, when comparing its historical volatility, Reynolds Consumer Products is 1.64 times less risky than TriMas. The stock trades about -0.16 of its potential returns per unit of risk. The TriMas is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 2,549 in TriMas on September 28, 2024 and sell it today you would lose (90.00) from holding TriMas or give up 3.53% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Reynolds Consumer Products vs. TriMas
Performance |
Timeline |
Reynolds Consumer |
TriMas |
Reynolds Consumer and TriMas Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Reynolds Consumer and TriMas
The main advantage of trading using opposite Reynolds Consumer and TriMas positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Reynolds Consumer position performs unexpectedly, TriMas 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 TriMas will offset losses from the drop in TriMas' long position.Reynolds Consumer vs. Greif Bros | Reynolds Consumer vs. Karat Packaging | Reynolds Consumer vs. Silgan Holdings | Reynolds Consumer vs. O I Glass |
TriMas vs. Greif Bros | TriMas vs. Karat Packaging | TriMas vs. Reynolds Consumer Products | TriMas vs. Silgan Holdings |
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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