Correlation Between Digihost Technology and BitFuFu
Can any of the company-specific risk be diversified away by investing in both Digihost Technology and BitFuFu 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 Digihost Technology and BitFuFu into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Digihost Technology and BitFuFu Class A, you can compare the effects of market volatilities on Digihost Technology and BitFuFu 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 Digihost Technology with a short position of BitFuFu. Check out your portfolio center. Please also check ongoing floating volatility patterns of Digihost Technology and BitFuFu.
Diversification Opportunities for Digihost Technology and BitFuFu
0.09 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Digihost and BitFuFu is 0.09. Overlapping area represents the amount of risk that can be diversified away by holding Digihost Technology and BitFuFu Class A in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BitFuFu Class A and Digihost Technology 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 Digihost Technology are associated (or correlated) with BitFuFu. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BitFuFu Class A has no effect on the direction of Digihost Technology i.e., Digihost Technology and BitFuFu go up and down completely randomly.
Pair Corralation between Digihost Technology and BitFuFu
Given the investment horizon of 90 days Digihost Technology is expected to generate 3.0 times less return on investment than BitFuFu. In addition to that, Digihost Technology is 1.76 times more volatile than BitFuFu Class A. It trades about 0.0 of its total potential returns per unit of risk. BitFuFu Class A is currently generating about 0.0 per unit of volatility. If you would invest 502.00 in BitFuFu Class A on December 30, 2024 and sell it today you would lose (34.00) from holding BitFuFu Class A or give up 6.77% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 85.48% |
Values | Daily Returns |
Digihost Technology vs. BitFuFu Class A
Performance |
Timeline |
Digihost Technology |
Risk-Adjusted Performance
Very Weak
Weak | Strong |
BitFuFu Class A |
Digihost Technology and BitFuFu Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Digihost Technology and BitFuFu
The main advantage of trading using opposite Digihost Technology and BitFuFu positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Digihost Technology position performs unexpectedly, BitFuFu 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 BitFuFu will offset losses from the drop in BitFuFu's long position.Digihost Technology vs. DatChat | Digihost Technology vs. Marin Software | Digihost Technology vs. Freight Technologies | Digihost Technology vs. Auddia Inc |
BitFuFu vs. Molecular Partners AG | BitFuFu vs. Radcom | BitFuFu vs. Nordic Semiconductor ASA | BitFuFu vs. MagnaChip Semiconductor |
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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