Correlation Between Amg Timessquare and Conestoga Micro
Can any of the company-specific risk be diversified away by investing in both Amg Timessquare and Conestoga Micro 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 Amg Timessquare and Conestoga Micro into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Amg Timessquare Mid and Conestoga Micro Cap, you can compare the effects of market volatilities on Amg Timessquare and Conestoga Micro 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 Amg Timessquare with a short position of Conestoga Micro. Check out your portfolio center. Please also check ongoing floating volatility patterns of Amg Timessquare and Conestoga Micro.
Diversification Opportunities for Amg Timessquare and Conestoga Micro
0.86 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Amg and Conestoga is 0.86. Overlapping area represents the amount of risk that can be diversified away by holding Amg Timessquare Mid and Conestoga Micro Cap in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Conestoga Micro Cap and Amg Timessquare 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 Amg Timessquare Mid are associated (or correlated) with Conestoga Micro. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Conestoga Micro Cap has no effect on the direction of Amg Timessquare i.e., Amg Timessquare and Conestoga Micro go up and down completely randomly.
Pair Corralation between Amg Timessquare and Conestoga Micro
Assuming the 90 days horizon Amg Timessquare is expected to generate 1.35 times less return on investment than Conestoga Micro. But when comparing it to its historical volatility, Amg Timessquare Mid is 1.77 times less risky than Conestoga Micro. It trades about 0.2 of its potential returns per unit of risk. Conestoga Micro Cap is currently generating about 0.15 of returns per unit of risk over similar time horizon. If you would invest 735.00 in Conestoga Micro Cap on September 5, 2024 and sell it today you would earn a total of 106.00 from holding Conestoga Micro Cap or generate 14.42% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Amg Timessquare Mid vs. Conestoga Micro Cap
Performance |
Timeline |
Amg Timessquare Mid |
Conestoga Micro Cap |
Amg Timessquare and Conestoga Micro Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Amg Timessquare and Conestoga Micro
The main advantage of trading using opposite Amg Timessquare and Conestoga Micro positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Amg Timessquare position performs unexpectedly, Conestoga Micro 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 Conestoga Micro will offset losses from the drop in Conestoga Micro's long position.Amg Timessquare vs. Amg Timessquare Small | Amg Timessquare vs. Amg Timessquare Mid | Amg Timessquare vs. Tcm Small Cap | Amg Timessquare vs. Small Pany Growth |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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