Correlation Between SBF 120 and Caisse Rgionale

Can any of the company-specific risk be diversified away by investing in both SBF 120 and Caisse Rgionale 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 SBF 120 and Caisse Rgionale into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between SBF 120 and Caisse Rgionale de, you can compare the effects of market volatilities on SBF 120 and Caisse Rgionale 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 SBF 120 with a short position of Caisse Rgionale. Check out your portfolio center. Please also check ongoing floating volatility patterns of SBF 120 and Caisse Rgionale.

Diversification Opportunities for SBF 120 and Caisse Rgionale

0.83

Correlation Coefficient

SBF 120  Caisse Rgionale

Very poor diversification

The 3 months correlation between SBF and Caisse is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding SBF 120 and Caisse Rgionale de in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Caisse Rgionale de and SBF 120 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 SBF 120 are associated (or correlated) with Caisse Rgionale. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Caisse Rgionale de has no effect on the direction of SBF 120 i.e., SBF 120 and Caisse Rgionale go up and down completely randomly.

Pair Corralation between SBF 120 and Caisse Rgionale

Assuming the 90 days trading horizon SBF 120 is expected to generate 1.26 times less return on investment than Caisse Rgionale. But when comparing it to its historical volatility, SBF 120 is 1.65 times less risky than Caisse Rgionale. It trades about 0.15 of its potential returns per unit of risk. Caisse Rgionale de is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest  6,580  in Caisse Rgionale de on December 30, 2024 and sell it today you would earn a total of  660.00  from holding Caisse Rgionale de or generate 10.03% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Strong
Accuracy	100.0%
Values	Daily Returns

SBF 120  vs.  Caisse Rgionale de

Pair Trading with SBF 120 and Caisse Rgionale

The main advantage of trading using opposite SBF 120 and Caisse Rgionale positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SBF 120 position performs unexpectedly, Caisse Rgionale 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 Caisse Rgionale will offset losses from the drop in Caisse Rgionale's long position.

The idea behind SBF 120 and Caisse Rgionale de 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.