Correlation Between Lam Research and Ultra Clean
Can any of the company-specific risk be diversified away by investing in both Lam Research and Ultra Clean 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 Lam Research and Ultra Clean into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lam Research and Ultra Clean Holdings, you can compare the effects of market volatilities on Lam Research and Ultra Clean 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 Lam Research with a short position of Ultra Clean. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lam Research and Ultra Clean.
Diversification Opportunities for Lam Research and Ultra Clean
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Lam and Ultra is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding Lam Research and Ultra Clean Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ultra Clean Holdings and Lam Research 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 Lam Research are associated (or correlated) with Ultra Clean. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ultra Clean Holdings has no effect on the direction of Lam Research i.e., Lam Research and Ultra Clean go up and down completely randomly.
Pair Corralation between Lam Research and Ultra Clean
Assuming the 90 days horizon Lam Research is expected to under-perform the Ultra Clean. But the stock apears to be less risky and, when comparing its historical volatility, Lam Research is 2.26 times less risky than Ultra Clean. The stock trades about -0.12 of its potential returns per unit of risk. The Ultra Clean Holdings is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest 3,240 in Ultra Clean Holdings on August 25, 2024 and sell it today you would earn a total of 140.00 from holding Ultra Clean Holdings or generate 4.32% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 95.45% |
| Values | Daily Returns |
Lam Research vs. Ultra Clean Holdings
Performance |
| Timeline |
| Lam Research |
| Ultra Clean Holdings |
Lam Research and Ultra Clean Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Lam Research and Ultra Clean
The main advantage of trading using opposite Lam Research and Ultra Clean positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lam Research position performs unexpectedly, Ultra Clean 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 Ultra Clean will offset losses from the drop in Ultra Clean's long position.| Lam Research vs. ASML HOLDING NY | Lam Research vs. Applied Materials | Lam Research vs. Superior Plus Corp | Lam Research vs. NMI Holdings |
| Ultra Clean vs. ASML HOLDING NY | Ultra Clean vs. Applied Materials | Ultra Clean vs. Lam Research | Ultra Clean vs. Superior Plus Corp |
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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