Correlation Between LARGAN Precision and Hon Hai
Can any of the company-specific risk be diversified away by investing in both LARGAN Precision and Hon Hai 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 LARGAN Precision and Hon Hai into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between LARGAN Precision Co and Hon Hai Precision, you can compare the effects of market volatilities on LARGAN Precision and Hon Hai 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 LARGAN Precision with a short position of Hon Hai. Check out your portfolio center. Please also check ongoing floating volatility patterns of LARGAN Precision and Hon Hai.
Diversification Opportunities for LARGAN Precision and Hon Hai
-0.74 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between LARGAN and Hon is -0.74. Overlapping area represents the amount of risk that can be diversified away by holding LARGAN Precision Co and Hon Hai Precision in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hon Hai Precision and LARGAN Precision 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 LARGAN Precision Co are associated (or correlated) with Hon Hai. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hon Hai Precision has no effect on the direction of LARGAN Precision i.e., LARGAN Precision and Hon Hai go up and down completely randomly.
Pair Corralation between LARGAN Precision and Hon Hai
Assuming the 90 days trading horizon LARGAN Precision Co is expected to under-perform the Hon Hai. In addition to that, LARGAN Precision is 1.21 times more volatile than Hon Hai Precision. It trades about -0.14 of its total potential returns per unit of risk. Hon Hai Precision is currently generating about 0.05 per unit of volatility. If you would invest 18,500 in Hon Hai Precision on September 3, 2024 and sell it today you would earn a total of 1,050 from holding Hon Hai Precision or generate 5.68% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
LARGAN Precision Co vs. Hon Hai Precision
Performance |
Timeline |
LARGAN Precision |
Hon Hai Precision |
LARGAN Precision and Hon Hai Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with LARGAN Precision and Hon Hai
The main advantage of trading using opposite LARGAN Precision and Hon Hai positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if LARGAN Precision position performs unexpectedly, Hon Hai 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 Hon Hai will offset losses from the drop in Hon Hai's long position.LARGAN Precision vs. MediaTek | LARGAN Precision vs. Hon Hai Precision | LARGAN Precision vs. Delta Electronics | LARGAN Precision vs. Catcher Technology Co |
Hon Hai vs. United Microelectronics | Hon Hai vs. MediaTek | Hon Hai vs. Chunghwa Telecom Co | Hon Hai vs. Delta Electronics |
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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