Correlation Between California Tax and Hunter Small
Can any of the company-specific risk be diversified away by investing in both California Tax and Hunter Small 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 California Tax and Hunter Small into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between California Tax Free Fund and Hunter Small Cap, you can compare the effects of market volatilities on California Tax and Hunter Small 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 California Tax with a short position of Hunter Small. Check out your portfolio center. Please also check ongoing floating volatility patterns of California Tax and Hunter Small.
Diversification Opportunities for California Tax and Hunter Small
0.4 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between California and Hunter is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding California Tax Free Fund and Hunter Small Cap in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hunter Small Cap and California Tax 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 California Tax Free Fund are associated (or correlated) with Hunter Small. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hunter Small Cap has no effect on the direction of California Tax i.e., California Tax and Hunter Small go up and down completely randomly.
Pair Corralation between California Tax and Hunter Small
Assuming the 90 days horizon California Tax is expected to generate 8.63 times less return on investment than Hunter Small. But when comparing it to its historical volatility, California Tax Free Fund is 5.04 times less risky than Hunter Small. It trades about 0.04 of its potential returns per unit of risk. Hunter Small Cap is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest 1,139 in Hunter Small Cap on September 29, 2024 and sell it today you would earn a total of 122.00 from holding Hunter Small Cap or generate 10.71% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
California Tax Free Fund vs. Hunter Small Cap
Performance |
Timeline |
California Tax Free |
Hunter Small Cap |
California Tax and Hunter Small Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with California Tax and Hunter Small
The main advantage of trading using opposite California Tax and Hunter Small positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if California Tax position performs unexpectedly, Hunter Small 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 Hunter Small will offset losses from the drop in Hunter Small's long position.California Tax vs. Scout Small Cap | California Tax vs. Hunter Small Cap | California Tax vs. Df Dent Small | California Tax vs. Vy Columbia Small |
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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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