Correlation Between Lebenthal Lisanti and Massmutual Select
Can any of the company-specific risk be diversified away by investing in both Lebenthal Lisanti and Massmutual Select 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 Lebenthal Lisanti and Massmutual Select into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lebenthal Lisanti Small and Massmutual Select T, you can compare the effects of market volatilities on Lebenthal Lisanti and Massmutual Select 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 Lebenthal Lisanti with a short position of Massmutual Select. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lebenthal Lisanti and Massmutual Select.
Diversification Opportunities for Lebenthal Lisanti and Massmutual Select
0.26 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Lebenthal and Massmutual is 0.26. Overlapping area represents the amount of risk that can be diversified away by holding Lebenthal Lisanti Small and Massmutual Select T in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Massmutual Select and Lebenthal Lisanti 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 Lebenthal Lisanti Small are associated (or correlated) with Massmutual Select. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Massmutual Select has no effect on the direction of Lebenthal Lisanti i.e., Lebenthal Lisanti and Massmutual Select go up and down completely randomly.
Pair Corralation between Lebenthal Lisanti and Massmutual Select
Assuming the 90 days horizon Lebenthal Lisanti Small is expected to generate 2.04 times more return on investment than Massmutual Select. However, Lebenthal Lisanti is 2.04 times more volatile than Massmutual Select T. It trades about 0.08 of its potential returns per unit of risk. Massmutual Select T is currently generating about -0.11 per unit of risk. If you would invest 2,041 in Lebenthal Lisanti Small on September 28, 2024 and sell it today you would earn a total of 132.00 from holding Lebenthal Lisanti Small or generate 6.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.41% |
Values | Daily Returns |
Lebenthal Lisanti Small vs. Massmutual Select T
Performance |
Timeline |
Lebenthal Lisanti Small |
Massmutual Select |
Lebenthal Lisanti and Massmutual Select Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lebenthal Lisanti and Massmutual Select
The main advantage of trading using opposite Lebenthal Lisanti and Massmutual Select positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lebenthal Lisanti position performs unexpectedly, Massmutual Select 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 Massmutual Select will offset losses from the drop in Massmutual Select's long position.Lebenthal Lisanti vs. Lord Abbett Growth | Lebenthal Lisanti vs. Queens Road Small | Lebenthal Lisanti vs. Eaton Vance Large Cap |
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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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