Correlation Between Ping An and Reliance Steel
Can any of the company-specific risk be diversified away by investing in both Ping An and Reliance Steel 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 Ping An and Reliance Steel into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ping An Healthcare and Reliance Steel Aluminum, you can compare the effects of market volatilities on Ping An and Reliance Steel 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 Ping An with a short position of Reliance Steel. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ping An and Reliance Steel.
Diversification Opportunities for Ping An and Reliance Steel
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Ping and Reliance is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Ping An Healthcare and Reliance Steel Aluminum in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Reliance Steel Aluminum and Ping An 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 Ping An Healthcare are associated (or correlated) with Reliance Steel. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Reliance Steel Aluminum has no effect on the direction of Ping An i.e., Ping An and Reliance Steel go up and down completely randomly.
Pair Corralation between Ping An and Reliance Steel
Assuming the 90 days horizon Ping An Healthcare is expected to generate 3.22 times more return on investment than Reliance Steel. However, Ping An is 3.22 times more volatile than Reliance Steel Aluminum. It trades about 0.11 of its potential returns per unit of risk. Reliance Steel Aluminum is currently generating about 0.18 per unit of risk. If you would invest 77.00 in Ping An Healthcare on April 25, 2025 and sell it today you would earn a total of 20.00 from holding Ping An Healthcare or generate 25.97% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Ping An Healthcare vs. Reliance Steel Aluminum
Performance |
Timeline |
Ping An Healthcare |
Reliance Steel Aluminum |
Ping An and Reliance Steel Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ping An and Reliance Steel
The main advantage of trading using opposite Ping An and Reliance Steel positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ping An position performs unexpectedly, Reliance Steel 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 Reliance Steel will offset losses from the drop in Reliance Steel's long position.Ping An vs. Laureate Education | Ping An vs. GURU ORGANIC ENERGY | Ping An vs. EBRO FOODS | Ping An vs. CAL MAINE FOODS |
Reliance Steel vs. HOCHSCHILD MINING | Reliance Steel vs. Forgame Holdings | Reliance Steel vs. UNIVERSAL DISPLAY | Reliance Steel vs. Universal Display |
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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