Correlation Between AUTO TRADER and Meta Platforms
Can any of the company-specific risk be diversified away by investing in both AUTO TRADER and Meta Platforms 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 AUTO TRADER and Meta Platforms into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between AUTO TRADER ADR and Meta Platforms, you can compare the effects of market volatilities on AUTO TRADER and Meta Platforms 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 AUTO TRADER with a short position of Meta Platforms. Check out your portfolio center. Please also check ongoing floating volatility patterns of AUTO TRADER and Meta Platforms.
Diversification Opportunities for AUTO TRADER and Meta Platforms
-0.24 | Correlation Coefficient |
Very good diversification
The 3 months correlation between AUTO and Meta is -0.24. Overlapping area represents the amount of risk that can be diversified away by holding AUTO TRADER ADR and Meta Platforms in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Meta Platforms and AUTO TRADER 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 AUTO TRADER ADR are associated (or correlated) with Meta Platforms. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Meta Platforms has no effect on the direction of AUTO TRADER i.e., AUTO TRADER and Meta Platforms go up and down completely randomly.
Pair Corralation between AUTO TRADER and Meta Platforms
Assuming the 90 days trading horizon AUTO TRADER ADR is expected to under-perform the Meta Platforms. But the stock apears to be less risky and, when comparing its historical volatility, AUTO TRADER ADR is 1.08 times less risky than Meta Platforms. The stock trades about -0.01 of its potential returns per unit of risk. The Meta Platforms is currently generating about 0.22 of returns per unit of risk over similar time horizon. If you would invest 46,950 in Meta Platforms on September 12, 2024 and sell it today you would earn a total of 11,780 from holding Meta Platforms or generate 25.09% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
AUTO TRADER ADR vs. Meta Platforms
Performance |
Timeline |
AUTO TRADER ADR |
Meta Platforms |
AUTO TRADER and Meta Platforms Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with AUTO TRADER and Meta Platforms
The main advantage of trading using opposite AUTO TRADER and Meta Platforms positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if AUTO TRADER position performs unexpectedly, Meta Platforms 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 Meta Platforms will offset losses from the drop in Meta Platforms' long position.AUTO TRADER vs. Tencent Holdings | AUTO TRADER vs. Superior Plus Corp | AUTO TRADER vs. SIVERS SEMICONDUCTORS AB | AUTO TRADER vs. NorAm Drilling AS |
Meta Platforms vs. Amazon Inc | Meta Platforms vs. Apple Inc | Meta Platforms vs. Meta Platforms | Meta Platforms vs. Microsoft |
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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