Correlation Between Big Time and DATA
Specify exactly 2 symbols:
By analyzing existing cross correlation between Big Time and DATA, you can compare the effects of market volatilities on Big Time and DATA 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 Big Time with a short position of DATA. Check out your portfolio center. Please also check ongoing floating volatility patterns of Big Time and DATA.
Diversification Opportunities for Big Time and DATA
Poor diversification
The 3 months correlation between Big and DATA is 0.78. Overlapping area represents the amount of risk that can be diversified away by holding Big Time and DATA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DATA and Big Time 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 Big Time are associated (or correlated) with DATA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DATA has no effect on the direction of Big Time i.e., Big Time and DATA go up and down completely randomly.
Pair Corralation between Big Time and DATA
Assuming the 90 days trading horizon Big Time is expected to under-perform the DATA. In addition to that, Big Time is 1.17 times more volatile than DATA. It trades about -0.44 of its total potential returns per unit of risk. DATA is currently generating about -0.5 per unit of volatility. If you would invest 4.36 in DATA on November 9, 2024 and sell it today you would lose (2.15) from holding DATA or give up 49.31% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Big Time vs. DATA
Performance |
| Timeline |
| Big Time |
| DATA |
Big Time and DATA Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Big Time and DATA
The main advantage of trading using opposite Big Time and DATA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Big Time position performs unexpectedly, DATA 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 DATA will offset losses from the drop in DATA's long position.The idea behind Big Time and DATA 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.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 Theme Ratings module to determine theme ratings based on digital equity recommendations. Macroaxis theme ratings are based on combination of fundamental analysis and risk-adjusted market performance.
Other Complementary Tools
| Portfolio Center All portfolio management and optimization tools to improve performance of your portfolios | |
| Top Crypto Exchanges Search and analyze digital assets across top global cryptocurrency exchanges | |
| Sectors List of equity sectors categorizing publicly traded companies based on their primary business activities | |
| Positions Ratings Determine portfolio positions ratings based on digital equity recommendations. Macroaxis instant position ratings are based on combination of fundamental analysis and risk-adjusted market performance | |
| Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm |