Correlation Between CleanTech Lithium and Dow Jones
Can any of the company-specific risk be diversified away by investing in both CleanTech Lithium and Dow Jones 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 CleanTech Lithium and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CleanTech Lithium plc and Dow Jones Industrial, you can compare the effects of market volatilities on CleanTech Lithium and Dow Jones 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 CleanTech Lithium with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of CleanTech Lithium and Dow Jones.
Diversification Opportunities for CleanTech Lithium and Dow Jones
0.15 | Correlation Coefficient |
Average diversification
The 3 months correlation between CleanTech and Dow is 0.15. Overlapping area represents the amount of risk that can be diversified away by holding CleanTech Lithium plc and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and CleanTech 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 CleanTech Lithium plc are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of CleanTech Lithium i.e., CleanTech Lithium and Dow Jones go up and down completely randomly.
Pair Corralation between CleanTech Lithium and Dow Jones
Assuming the 90 days trading horizon CleanTech Lithium plc is expected to under-perform the Dow Jones. In addition to that, CleanTech Lithium is 2.41 times more volatile than Dow Jones Industrial. It trades about -0.36 of its total potential returns per unit of risk. Dow Jones Industrial is currently generating about -0.27 per unit of volatility. If you would invest 4,440,193 in Dow Jones Industrial on October 10, 2024 and sell it today you would lose (187,357) from holding Dow Jones Industrial or give up 4.22% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 90.48% |
Values | Daily Returns |
CleanTech Lithium plc vs. Dow Jones Industrial
Performance |
Timeline |
CleanTech Lithium and Dow Jones Volatility Contrast
Predicted Return Density |
Returns |
CleanTech Lithium plc
Pair trading matchups for CleanTech Lithium
Dow Jones Industrial
Pair trading matchups for Dow Jones
Pair Trading with CleanTech Lithium and Dow Jones
The main advantage of trading using opposite CleanTech Lithium and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CleanTech Lithium position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.CleanTech Lithium vs. Charter Communications Cl | CleanTech Lithium vs. Futura Medical | CleanTech Lithium vs. McEwen Mining | CleanTech Lithium vs. Spirent Communications plc |
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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 Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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