Correlation Between Nan Ya and WIN Semiconductors
Can any of the company-specific risk be diversified away by investing in both Nan Ya and WIN Semiconductors 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 Nan Ya and WIN Semiconductors into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nan Ya Printed and WIN Semiconductors, you can compare the effects of market volatilities on Nan Ya and WIN Semiconductors 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 Nan Ya with a short position of WIN Semiconductors. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nan Ya and WIN Semiconductors.
Diversification Opportunities for Nan Ya and WIN Semiconductors
-0.09 | Correlation Coefficient |
Good diversification
The 3 months correlation between Nan and WIN is -0.09. Overlapping area represents the amount of risk that can be diversified away by holding Nan Ya Printed and WIN Semiconductors in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on WIN Semiconductors and Nan Ya 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 Nan Ya Printed are associated (or correlated) with WIN Semiconductors. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of WIN Semiconductors has no effect on the direction of Nan Ya i.e., Nan Ya and WIN Semiconductors go up and down completely randomly.
Pair Corralation between Nan Ya and WIN Semiconductors
Assuming the 90 days trading horizon Nan Ya is expected to generate 6.63 times less return on investment than WIN Semiconductors. In addition to that, Nan Ya is 1.13 times more volatile than WIN Semiconductors. It trades about 0.0 of its total potential returns per unit of risk. WIN Semiconductors is currently generating about 0.0 per unit of volatility. If you would invest 11,400 in WIN Semiconductors on December 28, 2024 and sell it today you would lose (150.00) from holding WIN Semiconductors or give up 1.32% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Nan Ya Printed vs. WIN Semiconductors
Performance |
Timeline |
Nan Ya Printed |
WIN Semiconductors |
Nan Ya and WIN Semiconductors Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Nan Ya and WIN Semiconductors
The main advantage of trading using opposite Nan Ya and WIN Semiconductors positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nan Ya position performs unexpectedly, WIN Semiconductors 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 WIN Semiconductors will offset losses from the drop in WIN Semiconductors' long position.Nan Ya vs. Unimicron Technology Corp | Nan Ya vs. Kinsus Interconnect Technology | Nan Ya vs. Novatek Microelectronics Corp | Nan Ya vs. Delta Electronics |
WIN Semiconductors vs. LARGAN Precision Co | WIN Semiconductors vs. GlobalWafers Co | WIN Semiconductors vs. Novatek Microelectronics Corp | WIN Semiconductors vs. Advanced Wireless Semiconductor |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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