Correlation Between Dunham Small and Dunham Focused
Can any of the company-specific risk be diversified away by investing in both Dunham Small and Dunham Focused 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 Dunham Small and Dunham Focused into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dunham Small Cap and Dunham Focused Large, you can compare the effects of market volatilities on Dunham Small and Dunham Focused 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 Dunham Small with a short position of Dunham Focused. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dunham Small and Dunham Focused.
Diversification Opportunities for Dunham Small and Dunham Focused
0.96 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Dunham and Dunham is 0.96. Overlapping area represents the amount of risk that can be diversified away by holding Dunham Small Cap and Dunham Focused Large in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dunham Focused Large and Dunham Small 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 Dunham Small Cap are associated (or correlated) with Dunham Focused. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dunham Focused Large has no effect on the direction of Dunham Small i.e., Dunham Small and Dunham Focused go up and down completely randomly.
Pair Corralation between Dunham Small and Dunham Focused
Assuming the 90 days horizon Dunham Small Cap is expected to under-perform the Dunham Focused. But the mutual fund apears to be less risky and, when comparing its historical volatility, Dunham Small Cap is 1.08 times less risky than Dunham Focused. The mutual fund trades about -0.18 of its potential returns per unit of risk. The Dunham Focused Large is currently generating about -0.11 of returns per unit of risk over similar time horizon. If you would invest 4,704 in Dunham Focused Large on November 28, 2024 and sell it today you would lose (657.00) from holding Dunham Focused Large or give up 13.97% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 98.31% |
Values | Daily Returns |
Dunham Small Cap vs. Dunham Focused Large
Performance |
Timeline |
Dunham Small Cap |
Dunham Focused Large |
Dunham Small and Dunham Focused Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Dunham Small and Dunham Focused
The main advantage of trading using opposite Dunham Small and Dunham Focused positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dunham Small position performs unexpectedly, Dunham Focused 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 Dunham Focused will offset losses from the drop in Dunham Focused's long position.Dunham Small vs. Transam Short Term Bond | Dunham Small vs. Ashmore Emerging Markets | Dunham Small vs. Transamerica Short Term Bond | Dunham Small vs. Blackrock Global Longshort |
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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