Correlation Between Binance Coin and CRAWFORD +
Can any of the company-specific risk be diversified away by investing in both Binance Coin and CRAWFORD + 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 Binance Coin and CRAWFORD + into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Binance Coin and CRAWFORD A NV, you can compare the effects of market volatilities on Binance Coin and CRAWFORD + 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 Binance Coin with a short position of CRAWFORD +. Check out your portfolio center. Please also check ongoing floating volatility patterns of Binance Coin and CRAWFORD +.
Diversification Opportunities for Binance Coin and CRAWFORD +
0.61 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Binance and CRAWFORD is 0.61. Overlapping area represents the amount of risk that can be diversified away by holding Binance Coin and CRAWFORD A NV in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CRAWFORD A NV and Binance Coin 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 Binance Coin are associated (or correlated) with CRAWFORD +. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CRAWFORD A NV has no effect on the direction of Binance Coin i.e., Binance Coin and CRAWFORD + go up and down completely randomly.
Pair Corralation between Binance Coin and CRAWFORD +
Assuming the 90 days trading horizon Binance Coin is expected to under-perform the CRAWFORD +. But the crypto coin apears to be less risky and, when comparing its historical volatility, Binance Coin is 1.16 times less risky than CRAWFORD +. The crypto coin trades about -0.03 of its potential returns per unit of risk. The CRAWFORD A NV is currently generating about -0.01 of returns per unit of risk over similar time horizon. If you would invest 1,050 in CRAWFORD A NV on October 11, 2024 and sell it today you would lose (10.00) from holding CRAWFORD A NV or give up 0.95% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 81.82% |
| Values | Daily Returns |
Binance Coin vs. CRAWFORD A NV
Performance |
| Timeline |
| Binance Coin |
| CRAWFORD A NV |
Binance Coin and CRAWFORD + Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Binance Coin and CRAWFORD +
The main advantage of trading using opposite Binance Coin and CRAWFORD + positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Binance Coin position performs unexpectedly, CRAWFORD + 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 CRAWFORD + will offset losses from the drop in CRAWFORD +'s long position.| Binance Coin vs. Staked Ether | Binance Coin vs. Cronos | Binance Coin vs. Wrapped Bitcoin | Binance Coin vs. Monero |
| CRAWFORD + vs. Forsys Metals Corp | CRAWFORD + vs. Aluminum of | CRAWFORD + vs. GALENA MINING LTD | CRAWFORD + vs. SERI INDUSTRIAL EO |
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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