Correlation Between Lithia Motors and Titan Machinery
Can any of the company-specific risk be diversified away by investing in both Lithia Motors and Titan Machinery 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 Lithia Motors and Titan Machinery into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lithia Motors and Titan Machinery, you can compare the effects of market volatilities on Lithia Motors and Titan Machinery 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 Lithia Motors with a short position of Titan Machinery. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lithia Motors and Titan Machinery.
Diversification Opportunities for Lithia Motors and Titan Machinery
0.37 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Lithia and Titan is 0.37. Overlapping area represents the amount of risk that can be diversified away by holding Lithia Motors and Titan Machinery in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Titan Machinery and Lithia Motors 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 Lithia Motors are associated (or correlated) with Titan Machinery. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Titan Machinery has no effect on the direction of Lithia Motors i.e., Lithia Motors and Titan Machinery go up and down completely randomly.
Pair Corralation between Lithia Motors and Titan Machinery
Considering the 90-day investment horizon Lithia Motors is expected to under-perform the Titan Machinery. But the stock apears to be less risky and, when comparing its historical volatility, Lithia Motors is 1.67 times less risky than Titan Machinery. The stock trades about -0.16 of its potential returns per unit of risk. The Titan Machinery is currently generating about 0.24 of returns per unit of risk over similar time horizon. If you would invest 1,382 in Titan Machinery on October 25, 2024 and sell it today you would earn a total of 155.50 from holding Titan Machinery or generate 11.25% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Lithia Motors vs. Titan Machinery
Performance |
Timeline |
Lithia Motors |
Titan Machinery |
Lithia Motors and Titan Machinery Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lithia Motors and Titan Machinery
The main advantage of trading using opposite Lithia Motors and Titan Machinery positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lithia Motors position performs unexpectedly, Titan Machinery 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 Titan Machinery will offset losses from the drop in Titan Machinery's long position.Lithia Motors vs. Sonic Automotive | Lithia Motors vs. AutoNation | Lithia Motors vs. Asbury Automotive Group | Lithia Motors vs. Penske Automotive Group |
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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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