Correlation Between Li Auto and Tradeweb Markets
Can any of the company-specific risk be diversified away by investing in both Li Auto and Tradeweb Markets 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 Li Auto and Tradeweb Markets into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Li Auto and Tradeweb Markets, you can compare the effects of market volatilities on Li Auto and Tradeweb Markets 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 Li Auto with a short position of Tradeweb Markets. Check out your portfolio center. Please also check ongoing floating volatility patterns of Li Auto and Tradeweb Markets.
Diversification Opportunities for Li Auto and Tradeweb Markets
0.64 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Li Auto and Tradeweb is 0.64. Overlapping area represents the amount of risk that can be diversified away by holding Li Auto and Tradeweb Markets in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tradeweb Markets and Li Auto 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 Li Auto are associated (or correlated) with Tradeweb Markets. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tradeweb Markets has no effect on the direction of Li Auto i.e., Li Auto and Tradeweb Markets go up and down completely randomly.
Pair Corralation between Li Auto and Tradeweb Markets
Allowing for the 90-day total investment horizon Li Auto is expected to generate 1.68 times less return on investment than Tradeweb Markets. In addition to that, Li Auto is 2.49 times more volatile than Tradeweb Markets. It trades about 0.02 of its total potential returns per unit of risk. Tradeweb Markets is currently generating about 0.1 per unit of volatility. If you would invest 6,503 in Tradeweb Markets on September 3, 2024 and sell it today you would earn a total of 6,945 from holding Tradeweb Markets or generate 106.8% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Li Auto vs. Tradeweb Markets
Performance |
| Timeline |
| Li Auto |
| Tradeweb Markets |
Li Auto and Tradeweb Markets Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Li Auto and Tradeweb Markets
The main advantage of trading using opposite Li Auto and Tradeweb Markets positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Li Auto position performs unexpectedly, Tradeweb Markets 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 Tradeweb Markets will offset losses from the drop in Tradeweb Markets' long position.| Li Auto vs. GreenPower Motor | Li Auto vs. ZEEKR Intelligent Technology | Li Auto vs. Volcon Inc | Li Auto vs. Ford Motor |
| Tradeweb Markets vs. Raymond James Financial | Tradeweb Markets vs. PJT Partners | Tradeweb Markets vs. Moelis Co | Tradeweb Markets vs. LPL Financial Holdings |
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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