Correlation Between CAC Consumer and Netmedia Group

Can any of the company-specific risk be diversified away by investing in both CAC Consumer and Netmedia Group 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 CAC Consumer and Netmedia Group into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between CAC Consumer Goods and Netmedia Group SA, you can compare the effects of market volatilities on CAC Consumer and Netmedia Group 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 CAC Consumer with a short position of Netmedia Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of CAC Consumer and Netmedia Group.

Diversification Opportunities for CAC Consumer and Netmedia Group

0.16

Correlation Coefficient

CAC Consumer  Netmedia Group

Average diversification

The 3 months correlation between CAC and Netmedia is 0.16. Overlapping area represents the amount of risk that can be diversified away by holding CAC Consumer Goods and Netmedia Group SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Netmedia Group SA and CAC Consumer 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 CAC Consumer Goods are associated (or correlated) with Netmedia Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Netmedia Group SA has no effect on the direction of CAC Consumer i.e., CAC Consumer and Netmedia Group go up and down completely randomly.

Pair Corralation between CAC Consumer and Netmedia Group

Assuming the 90 days trading horizon CAC Consumer Goods is expected to generate 0.24 times more return on investment than Netmedia Group. However, CAC Consumer Goods is 4.14 times less risky than Netmedia Group. It trades about 0.0 of its potential returns per unit of risk. Netmedia Group SA is currently generating about -0.04 per unit of risk. If you would invest  511,895  in CAC Consumer Goods on September 26, 2024 and sell it today you would lose (16,382) from holding CAC Consumer Goods or give up 3.2% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Insignificant
Accuracy	99.8%
Values	Daily Returns

CAC Consumer Goods  vs.  Netmedia Group SA

Pair Trading with CAC Consumer and Netmedia Group

The main advantage of trading using opposite CAC Consumer and Netmedia Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CAC Consumer position performs unexpectedly, Netmedia Group 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 Netmedia Group will offset losses from the drop in Netmedia Group's long position.

The idea behind CAC Consumer Goods and Netmedia Group SA 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.

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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.