Correlation Between BorgWarner and Dorman Products
Can any of the company-specific risk be diversified away by investing in both BorgWarner and Dorman Products 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 BorgWarner and Dorman Products into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BorgWarner and Dorman Products, you can compare the effects of market volatilities on BorgWarner and Dorman Products 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 BorgWarner with a short position of Dorman Products. Check out your portfolio center. Please also check ongoing floating volatility patterns of BorgWarner and Dorman Products.
Diversification Opportunities for BorgWarner and Dorman Products
0.4 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between BorgWarner and Dorman is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding BorgWarner and Dorman Products in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dorman Products and BorgWarner 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 BorgWarner are associated (or correlated) with Dorman Products. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dorman Products has no effect on the direction of BorgWarner i.e., BorgWarner and Dorman Products go up and down completely randomly.
Pair Corralation between BorgWarner and Dorman Products
Considering the 90-day investment horizon BorgWarner is expected to generate 1.03 times less return on investment than Dorman Products. But when comparing it to its historical volatility, BorgWarner is 1.17 times less risky than Dorman Products. It trades about 0.09 of its potential returns per unit of risk. Dorman Products is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest 12,955 in Dorman Products on November 1, 2024 and sell it today you would earn a total of 278.00 from holding Dorman Products or generate 2.15% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
BorgWarner vs. Dorman Products
Performance |
| Timeline |
| BorgWarner |
| Dorman Products |
BorgWarner and Dorman Products Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with BorgWarner and Dorman Products
The main advantage of trading using opposite BorgWarner and Dorman Products positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BorgWarner position performs unexpectedly, Dorman Products 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 Dorman Products will offset losses from the drop in Dorman Products' long position.| BorgWarner vs. Lear Corporation | BorgWarner vs. Autoliv | BorgWarner vs. Fox Factory Holding | BorgWarner vs. LKQ Corporation |
| Dorman Products vs. Standard Motor Products | Dorman Products vs. Motorcar Parts of | Dorman Products vs. Douglas Dynamics | Dorman Products vs. Stoneridge |
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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