Correlation Between Sentra Food and NYSE Composite

Can any of the company-specific risk be diversified away by investing in both Sentra Food and NYSE Composite 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 Sentra Food and NYSE Composite into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Sentra Food Indonesia and NYSE Composite, you can compare the effects of market volatilities on Sentra Food and NYSE Composite 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 Sentra Food with a short position of NYSE Composite. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sentra Food and NYSE Composite.

Diversification Opportunities for Sentra Food and NYSE Composite

-0.22

Correlation Coefficient

Sentra Food  NYSE Composite

Very good diversification

The 3 months correlation between Sentra and NYSE is -0.22. Overlapping area represents the amount of risk that can be diversified away by holding Sentra Food Indonesia and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Sentra Food 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 Sentra Food Indonesia are associated (or correlated) with NYSE Composite. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NYSE Composite has no effect on the direction of Sentra Food i.e., Sentra Food and NYSE Composite go up and down completely randomly.

Pair Corralation between Sentra Food and NYSE Composite

Assuming the 90 days trading horizon Sentra Food Indonesia is expected to generate 8.36 times more return on investment than NYSE Composite. However, Sentra Food is 8.36 times more volatile than NYSE Composite. It trades about 0.39 of its potential returns per unit of risk. NYSE Composite is currently generating about -0.18 per unit of risk. If you would invest  11,000  in Sentra Food Indonesia on January 29, 2024 and sell it today you would earn a total of  3,800  from holding Sentra Food Indonesia or generate 34.55% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Insignificant
Accuracy	71.43%
Values	Daily Returns

Sentra Food Indonesia  vs.  NYSE Composite

Pair Trading with Sentra Food and NYSE Composite

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

The idea behind Sentra Food Indonesia and NYSE Composite 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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.