Correlation Between Maple Leaf and Titanium Transportation
Can any of the company-specific risk be diversified away by investing in both Maple Leaf and Titanium Transportation 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 Maple Leaf and Titanium Transportation into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Maple Leaf Foods and Titanium Transportation Group, you can compare the effects of market volatilities on Maple Leaf and Titanium Transportation 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 Maple Leaf with a short position of Titanium Transportation. Check out your portfolio center. Please also check ongoing floating volatility patterns of Maple Leaf and Titanium Transportation.
Diversification Opportunities for Maple Leaf and Titanium Transportation
0.59 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Maple and Titanium is 0.59. Overlapping area represents the amount of risk that can be diversified away by holding Maple Leaf Foods and Titanium Transportation Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Titanium Transportation and Maple Leaf 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 Maple Leaf Foods are associated (or correlated) with Titanium Transportation. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Titanium Transportation has no effect on the direction of Maple Leaf i.e., Maple Leaf and Titanium Transportation go up and down completely randomly.
Pair Corralation between Maple Leaf and Titanium Transportation
Assuming the 90 days trading horizon Maple Leaf Foods is expected to generate 0.34 times more return on investment than Titanium Transportation. However, Maple Leaf Foods is 2.93 times less risky than Titanium Transportation. It trades about 0.3 of its potential returns per unit of risk. Titanium Transportation Group is currently generating about 0.08 per unit of risk. If you would invest 2,439 in Maple Leaf Foods on April 20, 2025 and sell it today you would earn a total of 598.00 from holding Maple Leaf Foods or generate 24.52% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Maple Leaf Foods vs. Titanium Transportation Group
Performance |
Timeline |
Maple Leaf Foods |
Titanium Transportation |
Maple Leaf and Titanium Transportation Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Maple Leaf and Titanium Transportation
The main advantage of trading using opposite Maple Leaf and Titanium Transportation positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Maple Leaf position performs unexpectedly, Titanium Transportation 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 Titanium Transportation will offset losses from the drop in Titanium Transportation's long position.Maple Leaf vs. Saputo Inc | Maple Leaf vs. George Weston Limited | Maple Leaf vs. Empire Company Limited | Maple Leaf vs. Premium Brands Holdings |
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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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