Correlation Between Victorias Secret and Haverty Furniture
Can any of the company-specific risk be diversified away by investing in both Victorias Secret and Haverty Furniture 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 Victorias Secret and Haverty Furniture into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Victorias Secret Co and Haverty Furniture Companies, you can compare the effects of market volatilities on Victorias Secret and Haverty Furniture 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 Victorias Secret with a short position of Haverty Furniture. Check out your portfolio center. Please also check ongoing floating volatility patterns of Victorias Secret and Haverty Furniture.
Diversification Opportunities for Victorias Secret and Haverty Furniture
0.62 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Victorias and Haverty is 0.62. Overlapping area represents the amount of risk that can be diversified away by holding Victorias Secret Co and Haverty Furniture Companies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Haverty Furniture and Victorias Secret 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 Victorias Secret Co are associated (or correlated) with Haverty Furniture. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Haverty Furniture has no effect on the direction of Victorias Secret i.e., Victorias Secret and Haverty Furniture go up and down completely randomly.
Pair Corralation between Victorias Secret and Haverty Furniture
Given the investment horizon of 90 days Victorias Secret Co is expected to under-perform the Haverty Furniture. In addition to that, Victorias Secret is 1.92 times more volatile than Haverty Furniture Companies. It trades about -0.17 of its total potential returns per unit of risk. Haverty Furniture Companies is currently generating about -0.07 per unit of volatility. If you would invest 2,139 in Haverty Furniture Companies on January 13, 2025 and sell it today you would lose (321.00) from holding Haverty Furniture Companies or give up 15.01% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Victorias Secret Co vs. Haverty Furniture Companies
Performance |
Timeline |
Victorias Secret |
Haverty Furniture |
Victorias Secret and Haverty Furniture Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Victorias Secret and Haverty Furniture
The main advantage of trading using opposite Victorias Secret and Haverty Furniture positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Victorias Secret position performs unexpectedly, Haverty Furniture 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 Haverty Furniture will offset losses from the drop in Haverty Furniture's long position.Victorias Secret vs. Ross Stores | Victorias Secret vs. Childrens Place | Victorias Secret vs. Buckle Inc | Victorias Secret vs. Guess Inc |
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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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