Correlation Between Hung Sheng and Silicon Power
Can any of the company-specific risk be diversified away by investing in both Hung Sheng and Silicon Power 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 Hung Sheng and Silicon Power into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hung Sheng Construction and Silicon Power Computer, you can compare the effects of market volatilities on Hung Sheng and Silicon Power 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 Hung Sheng with a short position of Silicon Power. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hung Sheng and Silicon Power.
Diversification Opportunities for Hung Sheng and Silicon Power
0.54 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Hung and Silicon is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding Hung Sheng Construction and Silicon Power Computer in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Silicon Power Computer and Hung Sheng 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 Hung Sheng Construction are associated (or correlated) with Silicon Power. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Silicon Power Computer has no effect on the direction of Hung Sheng i.e., Hung Sheng and Silicon Power go up and down completely randomly.
Pair Corralation between Hung Sheng and Silicon Power
Assuming the 90 days trading horizon Hung Sheng Construction is expected to under-perform the Silicon Power. But the stock apears to be less risky and, when comparing its historical volatility, Hung Sheng Construction is 1.0 times less risky than Silicon Power. The stock trades about -0.09 of its potential returns per unit of risk. The Silicon Power Computer is currently generating about -0.01 of returns per unit of risk over similar time horizon. If you would invest 3,225 in Silicon Power Computer on October 23, 2024 and sell it today you would lose (65.00) from holding Silicon Power Computer or give up 2.02% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Hung Sheng Construction vs. Silicon Power Computer
Performance |
Timeline |
Hung Sheng Construction |
Silicon Power Computer |
Hung Sheng and Silicon Power Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hung Sheng and Silicon Power
The main advantage of trading using opposite Hung Sheng and Silicon Power positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hung Sheng position performs unexpectedly, Silicon Power 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 Silicon Power will offset losses from the drop in Silicon Power's long position.Hung Sheng vs. Chainqui Construction Development | Hung Sheng vs. Kee Tai Properties | Hung Sheng vs. BES Engineering Co | Hung Sheng vs. Zinwell |
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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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