Correlation Between Plug Power and Siemens Aktiengesellscha
Can any of the company-specific risk be diversified away by investing in both Plug Power and Siemens Aktiengesellscha 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 Plug Power and Siemens Aktiengesellscha into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Plug Power and Siemens Aktiengesellschaft, you can compare the effects of market volatilities on Plug Power and Siemens Aktiengesellscha 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 Plug Power with a short position of Siemens Aktiengesellscha. Check out your portfolio center. Please also check ongoing floating volatility patterns of Plug Power and Siemens Aktiengesellscha.
Diversification Opportunities for Plug Power and Siemens Aktiengesellscha
0.65 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Plug and Siemens is 0.65. Overlapping area represents the amount of risk that can be diversified away by holding Plug Power and Siemens Aktiengesellschaft in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Siemens Aktiengesellscha and Plug Power 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 Plug Power are associated (or correlated) with Siemens Aktiengesellscha. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Siemens Aktiengesellscha has no effect on the direction of Plug Power i.e., Plug Power and Siemens Aktiengesellscha go up and down completely randomly.
Pair Corralation between Plug Power and Siemens Aktiengesellscha
Assuming the 90 days trading horizon Plug Power is expected to under-perform the Siemens Aktiengesellscha. In addition to that, Plug Power is 3.21 times more volatile than Siemens Aktiengesellschaft. It trades about -0.03 of its total potential returns per unit of risk. Siemens Aktiengesellschaft is currently generating about 0.03 per unit of volatility. If you would invest 17,428 in Siemens Aktiengesellschaft on September 2, 2024 and sell it today you would earn a total of 888.00 from holding Siemens Aktiengesellschaft or generate 5.1% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Plug Power vs. Siemens Aktiengesellschaft
Performance |
| Timeline |
| Plug Power |
| Siemens Aktiengesellscha |
Plug Power and Siemens Aktiengesellscha Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Plug Power and Siemens Aktiengesellscha
The main advantage of trading using opposite Plug Power and Siemens Aktiengesellscha positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Plug Power position performs unexpectedly, Siemens Aktiengesellscha 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 Siemens Aktiengesellscha will offset losses from the drop in Siemens Aktiengesellscha's long position.| Plug Power vs. Ballard Power Systems | Plug Power vs. Nel ASA | Plug Power vs. ITM Power Plc | Plug Power vs. Powercell Sweden |
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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