Correlation Between FTM and Axie Infinity
Can any of the company-specific risk be diversified away by investing in both FTM and Axie Infinity 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 FTM and Axie Infinity into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FTM and Axie Infinity Shards, you can compare the effects of market volatilities on FTM and Axie Infinity 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 FTM with a short position of Axie Infinity. Check out your portfolio center. Please also check ongoing floating volatility patterns of FTM and Axie Infinity.
Diversification Opportunities for FTM and Axie Infinity
0.46 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between FTM and Axie is 0.46. Overlapping area represents the amount of risk that can be diversified away by holding FTM and Axie Infinity Shards in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Axie Infinity Shards and FTM 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 FTM are associated (or correlated) with Axie Infinity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Axie Infinity Shards has no effect on the direction of FTM i.e., FTM and Axie Infinity go up and down completely randomly.
Pair Corralation between FTM and Axie Infinity
Assuming the 90 days trading horizon FTM is expected to generate 2.02 times more return on investment than Axie Infinity. However, FTM is 2.02 times more volatile than Axie Infinity Shards. It trades about 0.0 of its potential returns per unit of risk. Axie Infinity Shards is currently generating about -0.21 per unit of risk. If you would invest 68.00 in FTM on December 29, 2024 and sell it today you would lose (20.00) from holding FTM or give up 29.41% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 98.46% |
Values | Daily Returns |
FTM vs. Axie Infinity Shards
Performance |
Timeline |
FTM |
Axie Infinity Shards |
FTM and Axie Infinity Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with FTM and Axie Infinity
The main advantage of trading using opposite FTM and Axie Infinity positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FTM position performs unexpectedly, Axie Infinity 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 Axie Infinity will offset losses from the drop in Axie Infinity's long position.The idea behind FTM and Axie Infinity Shards pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.Axie Infinity vs. Staked Ether | Axie Infinity vs. Phala Network | Axie Infinity vs. EigenLayer | Axie Infinity vs. EOSDAC |
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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