Correlation Between Shihlin Electric and Kao Fong
Can any of the company-specific risk be diversified away by investing in both Shihlin Electric and Kao Fong 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 Shihlin Electric and Kao Fong into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shihlin Electric Engineering and Kao Fong Machinery, you can compare the effects of market volatilities on Shihlin Electric and Kao Fong 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 Shihlin Electric with a short position of Kao Fong. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shihlin Electric and Kao Fong.
Diversification Opportunities for Shihlin Electric and Kao Fong
-0.5 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Shihlin and Kao is -0.5. Overlapping area represents the amount of risk that can be diversified away by holding Shihlin Electric Engineering and Kao Fong Machinery in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kao Fong Machinery and Shihlin Electric 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 Shihlin Electric Engineering are associated (or correlated) with Kao Fong. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kao Fong Machinery has no effect on the direction of Shihlin Electric i.e., Shihlin Electric and Kao Fong go up and down completely randomly.
Pair Corralation between Shihlin Electric and Kao Fong
Assuming the 90 days trading horizon Shihlin Electric Engineering is expected to under-perform the Kao Fong. But the stock apears to be less risky and, when comparing its historical volatility, Shihlin Electric Engineering is 2.76 times less risky than Kao Fong. The stock trades about -0.1 of its potential returns per unit of risk. The Kao Fong Machinery is currently generating about 0.19 of returns per unit of risk over similar time horizon. If you would invest 4,365 in Kao Fong Machinery on October 9, 2024 and sell it today you would earn a total of 585.00 from holding Kao Fong Machinery or generate 13.4% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Shihlin Electric Engineering vs. Kao Fong Machinery
Performance |
Timeline |
Shihlin Electric Eng |
Kao Fong Machinery |
Shihlin Electric and Kao Fong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shihlin Electric and Kao Fong
The main advantage of trading using opposite Shihlin Electric and Kao Fong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shihlin Electric position performs unexpectedly, Kao Fong 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 Kao Fong will offset losses from the drop in Kao Fong's long position.Shihlin Electric vs. TECO Electric Machinery | Shihlin Electric vs. Walsin Lihwa Corp | Shihlin Electric vs. Formosa Taffeta Co | Shihlin Electric vs. Far Eastern New |
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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 Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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