Correlation Between Moong Pattana and Dow Jones

Can any of the company-specific risk be diversified away by investing in both Moong Pattana and Dow Jones 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 Moong Pattana and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Moong Pattana International and Dow Jones Industrial, you can compare the effects of market volatilities on Moong Pattana and Dow Jones 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 Moong Pattana with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Moong Pattana and Dow Jones.

Diversification Opportunities for Moong Pattana and Dow Jones

-0.08

Correlation Coefficient

Moong Pattana  Dow Jones

Good diversification

The 3 months correlation between Moong and Dow is -0.08. Overlapping area represents the amount of risk that can be diversified away by holding Moong Pattana International and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Moong Pattana 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 Moong Pattana International are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Moong Pattana i.e., Moong Pattana and Dow Jones go up and down completely randomly.

Pair Corralation between Moong Pattana and Dow Jones

Assuming the 90 days trading horizon Moong Pattana International is expected to generate 65.24 times more return on investment than Dow Jones. However, Moong Pattana is 65.24 times more volatile than Dow Jones Industrial. It trades about 0.04 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.08 per unit of risk. If you would invest  220.00  in Moong Pattana International on October 10, 2024 and sell it today you would lose (18.00) from holding Moong Pattana International or give up 8.18% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Insignificant
Accuracy	96.57%
Values	Daily Returns

Moong Pattana International  vs.  Dow Jones Industrial

Pair Trading with Moong Pattana and Dow Jones

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

The idea behind Moong Pattana International and Dow Jones Industrial 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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.