Correlation Between Basic Materials and Kinea Renda

Can any of the company-specific risk be diversified away by investing in both Basic Materials and Kinea Renda 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 Basic Materials and Kinea Renda into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Basic Materials and Kinea Renda Imobiliria, you can compare the effects of market volatilities on Basic Materials and Kinea Renda 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 Basic Materials with a short position of Kinea Renda. Check out your portfolio center. Please also check ongoing floating volatility patterns of Basic Materials and Kinea Renda.

Diversification Opportunities for Basic Materials and Kinea Renda

-0.59

Correlation Coefficient

Basic Materials  Kinea Renda

Excellent diversification

The 3 months correlation between Basic and Kinea is -0.59. Overlapping area represents the amount of risk that can be diversified away by holding Basic Materials and Kinea Renda Imobiliria in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kinea Renda Imobiliria and Basic Materials 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 Basic Materials are associated (or correlated) with Kinea Renda. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kinea Renda Imobiliria has no effect on the direction of Basic Materials i.e., Basic Materials and Kinea Renda go up and down completely randomly.

Pair Corralation between Basic Materials and Kinea Renda

Assuming the 90 days trading horizon Basic Materials is expected to under-perform the Kinea Renda. In addition to that, Basic Materials is 1.19 times more volatile than Kinea Renda Imobiliria. It trades about -0.16 of its total potential returns per unit of risk. Kinea Renda Imobiliria is currently generating about 0.1 per unit of volatility. If you would invest  12,507  in Kinea Renda Imobiliria on December 4, 2024 and sell it today you would earn a total of  805.00  from holding Kinea Renda Imobiliria or generate 6.44% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Very Weak
Accuracy	100.0%
Values	Daily Returns

Basic Materials  vs.  Kinea Renda Imobiliria

Pair Trading with Basic Materials and Kinea Renda

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

The idea behind Basic Materials and Kinea Renda Imobiliria 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.