Correlation Between Hudson Technologies and Codexis
Can any of the company-specific risk be diversified away by investing in both Hudson Technologies and Codexis 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 Hudson Technologies and Codexis into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hudson Technologies and Codexis, you can compare the effects of market volatilities on Hudson Technologies and Codexis 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 Hudson Technologies with a short position of Codexis. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hudson Technologies and Codexis.
Diversification Opportunities for Hudson Technologies and Codexis
-0.88 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Hudson and Codexis is -0.88. Overlapping area represents the amount of risk that can be diversified away by holding Hudson Technologies and Codexis in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Codexis and Hudson Technologies 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 Hudson Technologies are associated (or correlated) with Codexis. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Codexis has no effect on the direction of Hudson Technologies i.e., Hudson Technologies and Codexis go up and down completely randomly.
Pair Corralation between Hudson Technologies and Codexis
Given the investment horizon of 90 days Hudson Technologies is expected to under-perform the Codexis. But the stock apears to be less risky and, when comparing its historical volatility, Hudson Technologies is 1.89 times less risky than Codexis. The stock trades about -0.03 of its potential returns per unit of risk. The Codexis is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 256.00 in Codexis on August 31, 2024 and sell it today you would earn a total of 202.00 from holding Codexis or generate 78.91% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Hudson Technologies vs. Codexis
Performance |
| Timeline |
| Hudson Technologies |
| Codexis |
Hudson Technologies and Codexis Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Hudson Technologies and Codexis
The main advantage of trading using opposite Hudson Technologies and Codexis positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hudson Technologies position performs unexpectedly, Codexis 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 Codexis will offset losses from the drop in Codexis' long position.| Hudson Technologies vs. Sensient Technologies | Hudson Technologies vs. Innospec | Hudson Technologies vs. H B Fuller | Hudson Technologies vs. Quaker Chemical |
| Codexis vs. Nuvation Bio | Codexis vs. Lyell Immunopharma | Codexis vs. Century Therapeutics | Codexis vs. Generation Bio Co |
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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