Correlation Between GM and HAV Group
Can any of the company-specific risk be diversified away by investing in both GM and HAV Group 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 GM and HAV Group into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and HAV Group ASA, you can compare the effects of market volatilities on GM and HAV Group 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 GM with a short position of HAV Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and HAV Group.
Diversification Opportunities for GM and HAV Group
Poor diversification
The 3 months correlation between GM and HAV is 0.64. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and HAV Group ASA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on HAV Group ASA and GM 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 General Motors are associated (or correlated) with HAV Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of HAV Group ASA has no effect on the direction of GM i.e., GM and HAV Group go up and down completely randomly.
Pair Corralation between GM and HAV Group
Allowing for the 90-day total investment horizon General Motors is expected to under-perform the HAV Group. In addition to that, GM is 1.13 times more volatile than HAV Group ASA. It trades about -0.34 of its total potential returns per unit of risk. HAV Group ASA is currently generating about -0.14 per unit of volatility. If you would invest 602.00 in HAV Group ASA on November 27, 2024 and sell it today you would lose (38.00) from holding HAV Group ASA or give up 6.31% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.24% |
| Values | Daily Returns |
General Motors vs. HAV Group ASA
Performance |
| Timeline |
| General Motors |
| HAV Group ASA |
GM and HAV Group Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and HAV Group
The main advantage of trading using opposite GM and HAV Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, HAV Group 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 HAV Group will offset losses from the drop in HAV Group's long position.The idea behind General Motors and HAV Group ASA pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| HAV Group vs. Sogn Sparebank | HAV Group vs. SpareBank 1 stlandet | HAV Group vs. Sparebank 1 SMN | HAV Group vs. Pareto Bank ASA |
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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