Correlation Between TAL Education and MAHLE Metal
Can any of the company-specific risk be diversified away by investing in both TAL Education and MAHLE Metal 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 TAL Education and MAHLE Metal into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between TAL Education Group and MAHLE Metal Leve, you can compare the effects of market volatilities on TAL Education and MAHLE Metal 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 TAL Education with a short position of MAHLE Metal. Check out your portfolio center. Please also check ongoing floating volatility patterns of TAL Education and MAHLE Metal.
Diversification Opportunities for TAL Education and MAHLE Metal
-0.48 | Correlation Coefficient |
Very good diversification
The 3 months correlation between TAL and MAHLE is -0.48. Overlapping area represents the amount of risk that can be diversified away by holding TAL Education Group and MAHLE Metal Leve in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on MAHLE Metal Leve and TAL Education 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 TAL Education Group are associated (or correlated) with MAHLE Metal. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of MAHLE Metal Leve has no effect on the direction of TAL Education i.e., TAL Education and MAHLE Metal go up and down completely randomly.
Pair Corralation between TAL Education and MAHLE Metal
Assuming the 90 days trading horizon TAL Education Group is expected to under-perform the MAHLE Metal. In addition to that, TAL Education is 1.45 times more volatile than MAHLE Metal Leve. It trades about -0.19 of its total potential returns per unit of risk. MAHLE Metal Leve is currently generating about -0.16 per unit of volatility. If you would invest 2,966 in MAHLE Metal Leve on August 26, 2024 and sell it today you would lose (172.00) from holding MAHLE Metal Leve or give up 5.8% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
TAL Education Group vs. MAHLE Metal Leve
Performance |
| Timeline |
| TAL Education Group |
| MAHLE Metal Leve |
TAL Education and MAHLE Metal Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with TAL Education and MAHLE Metal
The main advantage of trading using opposite TAL Education and MAHLE Metal positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if TAL Education position performs unexpectedly, MAHLE Metal 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 MAHLE Metal will offset losses from the drop in MAHLE Metal's long position.| TAL Education vs. Fras le SA | TAL Education vs. Clave Indices De | TAL Education vs. BTG Pactual Logstica | TAL Education vs. Telefonaktiebolaget LM Ericsson |
| MAHLE Metal vs. Tupy SA | MAHLE Metal vs. Engie Brasil Energia | MAHLE Metal vs. Grendene SA | MAHLE Metal vs. M Dias Branco |
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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