Correlation Between Nova Lithium and POWR Lithium
Can any of the company-specific risk be diversified away by investing in both Nova Lithium and POWR Lithium 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 Nova Lithium and POWR Lithium into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nova Lithium Corp and POWR Lithium Corp, you can compare the effects of market volatilities on Nova Lithium and POWR Lithium 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 Nova Lithium with a short position of POWR Lithium. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nova Lithium and POWR Lithium.
Diversification Opportunities for Nova Lithium and POWR Lithium
-0.05 | Correlation Coefficient |
Good diversification
The 3 months correlation between Nova and POWR is -0.05. Overlapping area represents the amount of risk that can be diversified away by holding Nova Lithium Corp and POWR Lithium Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on POWR Lithium Corp and Nova Lithium 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 Nova Lithium Corp are associated (or correlated) with POWR Lithium. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of POWR Lithium Corp has no effect on the direction of Nova Lithium i.e., Nova Lithium and POWR Lithium go up and down completely randomly.
Pair Corralation between Nova Lithium and POWR Lithium
Assuming the 90 days horizon Nova Lithium is expected to generate 776.88 times less return on investment than POWR Lithium. But when comparing it to its historical volatility, Nova Lithium Corp is 8.91 times less risky than POWR Lithium. It trades about 0.0 of its potential returns per unit of risk. POWR Lithium Corp is currently generating about 0.13 of returns per unit of risk over similar time horizon. If you would invest 4.74 in POWR Lithium Corp on September 4, 2024 and sell it today you would earn a total of 1.16 from holding POWR Lithium Corp or generate 24.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.46% |
Values | Daily Returns |
Nova Lithium Corp vs. POWR Lithium Corp
Performance |
Timeline |
Nova Lithium Corp |
POWR Lithium Corp |
Nova Lithium and POWR Lithium Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Nova Lithium and POWR Lithium
The main advantage of trading using opposite Nova Lithium and POWR Lithium positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nova Lithium position performs unexpectedly, POWR Lithium 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 POWR Lithium will offset losses from the drop in POWR Lithium's long position.Nova Lithium vs. Apple Inc | Nova Lithium vs. Microsoft | Nova Lithium vs. Amazon Inc | Nova Lithium vs. Alphabet Inc Class C |
POWR Lithium vs. Apple Inc | POWR Lithium vs. Microsoft | POWR Lithium vs. Amazon Inc | POWR Lithium vs. Alphabet Inc Class C |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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