Correlation Between Saha Pathanapibul and Berli Jucker
Can any of the company-specific risk be diversified away by investing in both Saha Pathanapibul and Berli Jucker 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 Saha Pathanapibul and Berli Jucker into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Saha Pathanapibul Public and Berli Jucker Public, you can compare the effects of market volatilities on Saha Pathanapibul and Berli Jucker 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 Saha Pathanapibul with a short position of Berli Jucker. Check out your portfolio center. Please also check ongoing floating volatility patterns of Saha Pathanapibul and Berli Jucker.
Diversification Opportunities for Saha Pathanapibul and Berli Jucker
-0.12 | Correlation Coefficient |
Good diversification
The 3 months correlation between Saha and Berli is -0.12. Overlapping area represents the amount of risk that can be diversified away by holding Saha Pathanapibul Public and Berli Jucker Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Berli Jucker Public and Saha Pathanapibul 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 Saha Pathanapibul Public are associated (or correlated) with Berli Jucker. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Berli Jucker Public has no effect on the direction of Saha Pathanapibul i.e., Saha Pathanapibul and Berli Jucker go up and down completely randomly.
Pair Corralation between Saha Pathanapibul and Berli Jucker
Assuming the 90 days trading horizon Saha Pathanapibul Public is expected to generate 0.31 times more return on investment than Berli Jucker. However, Saha Pathanapibul Public is 3.26 times less risky than Berli Jucker. It trades about -0.01 of its potential returns per unit of risk. Berli Jucker Public is currently generating about -0.03 per unit of risk. If you would invest 6,039 in Saha Pathanapibul Public on September 12, 2024 and sell it today you would lose (214.00) from holding Saha Pathanapibul Public or give up 3.54% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Saha Pathanapibul Public vs. Berli Jucker Public
Performance |
| Timeline |
| Saha Pathanapibul Public |
| Berli Jucker Public |
Saha Pathanapibul and Berli Jucker Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Saha Pathanapibul and Berli Jucker
The main advantage of trading using opposite Saha Pathanapibul and Berli Jucker positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Saha Pathanapibul position performs unexpectedly, Berli Jucker 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 Berli Jucker will offset losses from the drop in Berli Jucker's long position.| Saha Pathanapibul vs. Saha Pathana Inter Holding | Saha Pathanapibul vs. Berli Jucker Public | Saha Pathanapibul vs. Quality Houses Public | Saha Pathanapibul vs. President Bakery 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 Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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