Correlation Between IDX 30 and Sentra Food

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Can any of the company-specific risk be diversified away by investing in both IDX 30 and Sentra Food 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 IDX 30 and Sentra Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between IDX 30 Jakarta and Sentra Food Indonesia, you can compare the effects of market volatilities on IDX 30 and Sentra Food 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 IDX 30 with a short position of Sentra Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of IDX 30 and Sentra Food.

Diversification Opportunities for IDX 30 and Sentra Food

0.6
  Correlation Coefficient

Poor diversification

The 3 months correlation between IDX and Sentra is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding IDX 30 Jakarta and Sentra Food Indonesia in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sentra Food Indonesia and IDX 30 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 IDX 30 Jakarta are associated (or correlated) with Sentra Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sentra Food Indonesia has no effect on the direction of IDX 30 i.e., IDX 30 and Sentra Food go up and down completely randomly.
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Pair Corralation between IDX 30 and Sentra Food

Assuming the 90 days trading horizon IDX 30 Jakarta is expected to under-perform the Sentra Food. But the index apears to be less risky and, when comparing its historical volatility, IDX 30 Jakarta is 1.24 times less risky than Sentra Food. The index trades about -0.15 of its potential returns per unit of risk. The Sentra Food Indonesia is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest  12,200  in Sentra Food Indonesia on September 15, 2024 and sell it today you would lose (300.00) from holding Sentra Food Indonesia or give up 2.46% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthSignificant
Accuracy100.0%
ValuesDaily Returns

IDX 30 Jakarta  vs.  Sentra Food Indonesia

 Performance 
       Timeline  

IDX 30 and Sentra Food Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with IDX 30 and Sentra Food

The main advantage of trading using opposite IDX 30 and Sentra Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if IDX 30 position performs unexpectedly, Sentra Food 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 Sentra Food will offset losses from the drop in Sentra Food's long position.
The idea behind IDX 30 Jakarta and Sentra Food Indonesia 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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.

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