Correlation Between SPARTAN STORES and PICKN PAY
Can any of the company-specific risk be diversified away by investing in both SPARTAN STORES and PICKN PAY 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 SPARTAN STORES and PICKN PAY into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SPARTAN STORES and PICKN PAY STORES, you can compare the effects of market volatilities on SPARTAN STORES and PICKN PAY 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 SPARTAN STORES with a short position of PICKN PAY. Check out your portfolio center. Please also check ongoing floating volatility patterns of SPARTAN STORES and PICKN PAY.
Diversification Opportunities for SPARTAN STORES and PICKN PAY
-0.07 | Correlation Coefficient |
Good diversification
The 3 months correlation between SPARTAN and PICKN is -0.07. Overlapping area represents the amount of risk that can be diversified away by holding SPARTAN STORES and PICKN PAY STORES in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PICKN PAY STORES and SPARTAN STORES 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 SPARTAN STORES are associated (or correlated) with PICKN PAY. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PICKN PAY STORES has no effect on the direction of SPARTAN STORES i.e., SPARTAN STORES and PICKN PAY go up and down completely randomly.
Pair Corralation between SPARTAN STORES and PICKN PAY
Assuming the 90 days trading horizon SPARTAN STORES is expected to generate 1.44 times more return on investment than PICKN PAY. However, SPARTAN STORES is 1.44 times more volatile than PICKN PAY STORES. It trades about 0.2 of its potential returns per unit of risk. PICKN PAY STORES is currently generating about 0.04 per unit of risk. If you would invest 1,690 in SPARTAN STORES on November 28, 2024 and sell it today you would earn a total of 220.00 from holding SPARTAN STORES or generate 13.02% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 95.65% |
| Values | Daily Returns |
SPARTAN STORES vs. PICKN PAY STORES
Performance |
| Timeline |
| SPARTAN STORES |
| PICKN PAY STORES |
SPARTAN STORES and PICKN PAY Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with SPARTAN STORES and PICKN PAY
The main advantage of trading using opposite SPARTAN STORES and PICKN PAY positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SPARTAN STORES position performs unexpectedly, PICKN PAY 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 PICKN PAY will offset losses from the drop in PICKN PAY's long position.| SPARTAN STORES vs. ANGANG STEEL H | SPARTAN STORES vs. CALTAGIRONE EDITORE | SPARTAN STORES vs. COMBA TELECOM SYST | SPARTAN STORES vs. TOMBADOR IRON LTD |
| PICKN PAY vs. Retail Estates NV | PICKN PAY vs. CANON MARKETING JP | PICKN PAY vs. BOS BETTER ONLINE | PICKN PAY vs. YATRA ONLINE DL 0001 |
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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