Correlation Between Betagro PCL and Moshi Moshi
Can any of the company-specific risk be diversified away by investing in both Betagro PCL and Moshi Moshi 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 Betagro PCL and Moshi Moshi into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Betagro PCL and Moshi Moshi Retail, you can compare the effects of market volatilities on Betagro PCL and Moshi Moshi 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 Betagro PCL with a short position of Moshi Moshi. Check out your portfolio center. Please also check ongoing floating volatility patterns of Betagro PCL and Moshi Moshi.
Diversification Opportunities for Betagro PCL and Moshi Moshi
0.75 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Betagro and Moshi is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding Betagro PCL and Moshi Moshi Retail in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Moshi Moshi Retail and Betagro PCL 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 Betagro PCL are associated (or correlated) with Moshi Moshi. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Moshi Moshi Retail has no effect on the direction of Betagro PCL i.e., Betagro PCL and Moshi Moshi go up and down completely randomly.
Pair Corralation between Betagro PCL and Moshi Moshi
Assuming the 90 days trading horizon Betagro PCL is expected to under-perform the Moshi Moshi. But the stock apears to be less risky and, when comparing its historical volatility, Betagro PCL is 2.45 times less risky than Moshi Moshi. The stock trades about -0.26 of its potential returns per unit of risk. The Moshi Moshi Retail is currently generating about -0.1 of returns per unit of risk over similar time horizon. If you would invest 4,400 in Moshi Moshi Retail on October 24, 2024 and sell it today you would lose (250.00) from holding Moshi Moshi Retail or give up 5.68% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.24% |
| Values | Daily Returns |
Betagro PCL vs. Moshi Moshi Retail
Performance |
| Timeline |
| Betagro PCL |
| Moshi Moshi Retail |
Betagro PCL and Moshi Moshi Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Betagro PCL and Moshi Moshi
The main advantage of trading using opposite Betagro PCL and Moshi Moshi positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Betagro PCL position performs unexpectedly, Moshi Moshi 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 Moshi Moshi will offset losses from the drop in Moshi Moshi's long position.| Betagro PCL vs. Thai Life Insurance | Betagro PCL vs. Thai Union Group | Betagro PCL vs. SCG Packaging Public | Betagro PCL vs. Gulf Energy Development |
| Moshi Moshi vs. PTT Oil and | Moshi Moshi vs. Delta Electronics Public | Moshi Moshi vs. Com7 PCL | Moshi Moshi vs. Betagro PCL |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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