Correlation Between X Financial and Pfg Invesco
Can any of the company-specific risk be diversified away by investing in both X Financial and Pfg Invesco 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 X Financial and Pfg Invesco into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between X Financial Class and Pfg Invesco Thematic, you can compare the effects of market volatilities on X Financial and Pfg Invesco 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 X Financial with a short position of Pfg Invesco. Check out your portfolio center. Please also check ongoing floating volatility patterns of X Financial and Pfg Invesco.
Diversification Opportunities for X Financial and Pfg Invesco
-0.17 | Correlation Coefficient |
Good diversification
The 3 months correlation between XYF and Pfg is -0.17. Overlapping area represents the amount of risk that can be diversified away by holding X Financial Class and Pfg Invesco Thematic in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pfg Invesco Thematic and X Financial 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 X Financial Class are associated (or correlated) with Pfg Invesco. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pfg Invesco Thematic has no effect on the direction of X Financial i.e., X Financial and Pfg Invesco go up and down completely randomly.
Pair Corralation between X Financial and Pfg Invesco
Considering the 90-day investment horizon X Financial Class is expected to generate 6.15 times more return on investment than Pfg Invesco. However, X Financial is 6.15 times more volatile than Pfg Invesco Thematic. It trades about 0.13 of its potential returns per unit of risk. Pfg Invesco Thematic is currently generating about -0.03 per unit of risk. If you would invest 622.00 in X Financial Class on October 19, 2024 and sell it today you would earn a total of 121.00 from holding X Financial Class or generate 19.45% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
X Financial Class vs. Pfg Invesco Thematic
Performance |
Timeline |
X Financial Class |
Pfg Invesco Thematic |
X Financial and Pfg Invesco Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with X Financial and Pfg Invesco
The main advantage of trading using opposite X Financial and Pfg Invesco positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if X Financial position performs unexpectedly, Pfg Invesco 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 Pfg Invesco will offset losses from the drop in Pfg Invesco's long position.X Financial vs. LM Funding America | X Financial vs. Nisun International Enterprise | X Financial vs. Qudian Inc | X Financial vs. FinVolution Group |
Pfg Invesco vs. Riskproreg Pfg 0 15 | Pfg Invesco vs. Pfg American Funds | Pfg Invesco vs. Pfg Br Equity | Pfg Invesco vs. Riskproreg Dynamic 0 10 |
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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