Correlation Between Nawarat Patanakarn and Pato Chemical
Can any of the company-specific risk be diversified away by investing in both Nawarat Patanakarn and Pato Chemical 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 Nawarat Patanakarn and Pato Chemical into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nawarat Patanakarn Public and Pato Chemical Industry, you can compare the effects of market volatilities on Nawarat Patanakarn and Pato Chemical 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 Nawarat Patanakarn with a short position of Pato Chemical. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nawarat Patanakarn and Pato Chemical.
Diversification Opportunities for Nawarat Patanakarn and Pato Chemical
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Nawarat and Pato is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Nawarat Patanakarn Public and Pato Chemical Industry in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pato Chemical Industry and Nawarat Patanakarn 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 Nawarat Patanakarn Public are associated (or correlated) with Pato Chemical. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pato Chemical Industry has no effect on the direction of Nawarat Patanakarn i.e., Nawarat Patanakarn and Pato Chemical go up and down completely randomly.
Pair Corralation between Nawarat Patanakarn and Pato Chemical
Assuming the 90 days trading horizon Nawarat Patanakarn Public is expected to under-perform the Pato Chemical. In addition to that, Nawarat Patanakarn is 5.31 times more volatile than Pato Chemical Industry. It trades about -0.14 of its total potential returns per unit of risk. Pato Chemical Industry is currently generating about -0.24 per unit of volatility. If you would invest 925.00 in Pato Chemical Industry on September 15, 2024 and sell it today you would lose (90.00) from holding Pato Chemical Industry or give up 9.73% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 96.72% |
Values | Daily Returns |
Nawarat Patanakarn Public vs. Pato Chemical Industry
Performance |
Timeline |
Nawarat Patanakarn Public |
Pato Chemical Industry |
Nawarat Patanakarn and Pato Chemical Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Nawarat Patanakarn and Pato Chemical
The main advantage of trading using opposite Nawarat Patanakarn and Pato Chemical positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nawarat Patanakarn position performs unexpectedly, Pato Chemical 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 Pato Chemical will offset losses from the drop in Pato Chemical's long position.Nawarat Patanakarn vs. Tata Steel Public | Nawarat Patanakarn vs. TTCL Public | Nawarat Patanakarn vs. Thaifoods Group Public | Nawarat Patanakarn vs. TMT Steel Public |
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Check out your portfolio center.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.
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