Correlation Between Nova and Willy Food
Can any of the company-specific risk be diversified away by investing in both Nova and Willy Food 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 Nova and Willy Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nova and Willy Food, you can compare the effects of market volatilities on Nova and Willy Food 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 Nova with a short position of Willy Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nova and Willy Food.
Diversification Opportunities for Nova and Willy Food
0.63 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Nova and Willy is 0.63. Overlapping area represents the amount of risk that can be diversified away by holding Nova and Willy Food in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Willy Food and Nova 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 Nova are associated (or correlated) with Willy Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Willy Food has no effect on the direction of Nova i.e., Nova and Willy Food go up and down completely randomly.
Pair Corralation between Nova and Willy Food
Assuming the 90 days trading horizon Nova is expected to under-perform the Willy Food. But the stock apears to be less risky and, when comparing its historical volatility, Nova is 1.0 times less risky than Willy Food. The stock trades about -0.2 of its potential returns per unit of risk. The Willy Food is currently generating about 0.0 of returns per unit of risk over similar time horizon. If you would invest 277,100 in Willy Food on January 14, 2025 and sell it today you would lose (2,100) from holding Willy Food or give up 0.76% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Nova vs. Willy Food
Performance |
| Timeline |
| Nova |
| Willy Food |
Nova and Willy Food Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Nova and Willy Food
The main advantage of trading using opposite Nova and Willy Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nova position performs unexpectedly, Willy Food 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 Willy Food will offset losses from the drop in Willy Food's long position.The idea behind Nova and Willy Food 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.| Willy Food vs. Rami Levi | Willy Food vs. Neto ME Holdings | Willy Food vs. Shufersal | Willy Food vs. Strauss Group |
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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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