Correlation Between Kirklands and Tile Shop
Can any of the company-specific risk be diversified away by investing in both Kirklands and Tile Shop 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 Kirklands and Tile Shop into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kirklands and Tile Shop Holdings, you can compare the effects of market volatilities on Kirklands and Tile Shop 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 Kirklands with a short position of Tile Shop. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kirklands and Tile Shop.
Diversification Opportunities for Kirklands and Tile Shop
Very weak diversification
The 3 months correlation between Kirklands and Tile is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding Kirklands and Tile Shop Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tile Shop Holdings and Kirklands 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 Kirklands are associated (or correlated) with Tile Shop. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tile Shop Holdings has no effect on the direction of Kirklands i.e., Kirklands and Tile Shop go up and down completely randomly.
Pair Corralation between Kirklands and Tile Shop
Given the investment horizon of 90 days Kirklands is expected to under-perform the Tile Shop. In addition to that, Kirklands is 1.59 times more volatile than Tile Shop Holdings. It trades about -0.16 of its total potential returns per unit of risk. Tile Shop Holdings is currently generating about 0.02 per unit of volatility. If you would invest 658.00 in Tile Shop Holdings on August 25, 2024 and sell it today you would earn a total of 3.00 from holding Tile Shop Holdings or generate 0.46% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Kirklands vs. Tile Shop Holdings
Performance |
| Timeline |
| Kirklands |
| Tile Shop Holdings |
Kirklands and Tile Shop Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Kirklands and Tile Shop
The main advantage of trading using opposite Kirklands and Tile Shop positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kirklands position performs unexpectedly, Tile Shop 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 Tile Shop will offset losses from the drop in Tile Shop's long position.The idea behind Kirklands and Tile Shop Holdings pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.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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