Correlation Between TISCO Financial and SCB X
Can any of the company-specific risk be diversified away by investing in both TISCO Financial and SCB X 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 TISCO Financial and SCB X into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between TISCO Financial Group and SCB X Public, you can compare the effects of market volatilities on TISCO Financial and SCB X 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 TISCO Financial with a short position of SCB X. Check out your portfolio center. Please also check ongoing floating volatility patterns of TISCO Financial and SCB X.
Diversification Opportunities for TISCO Financial and SCB X
0.1 | Correlation Coefficient |
Average diversification
The 3 months correlation between TISCO and SCB is 0.1. Overlapping area represents the amount of risk that can be diversified away by holding TISCO Financial Group and SCB X Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SCB X Public and TISCO Financial 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 TISCO Financial Group are associated (or correlated) with SCB X. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SCB X Public has no effect on the direction of TISCO Financial i.e., TISCO Financial and SCB X go up and down completely randomly.
Pair Corralation between TISCO Financial and SCB X
Assuming the 90 days trading horizon TISCO Financial is expected to generate 8.76 times less return on investment than SCB X. But when comparing it to its historical volatility, TISCO Financial Group is 1.91 times less risky than SCB X. It trades about 0.02 of its potential returns per unit of risk. SCB X Public is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest 11,300 in SCB X Public on September 12, 2024 and sell it today you would earn a total of 550.00 from holding SCB X Public or generate 4.87% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 98.39% |
Values | Daily Returns |
TISCO Financial Group vs. SCB X Public
Performance |
Timeline |
TISCO Financial Group |
SCB X Public |
TISCO Financial and SCB X Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with TISCO Financial and SCB X
The main advantage of trading using opposite TISCO Financial and SCB X positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if TISCO Financial position performs unexpectedly, SCB X 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 SCB X will offset losses from the drop in SCB X's long position.TISCO Financial vs. KGI Securities Public | TISCO Financial vs. Lalin Property Public | TISCO Financial vs. Hwa Fong Rubber | TISCO Financial vs. MCS Steel Public |
SCB X vs. Information and Communication | SCB X vs. Union Petrochemical Public | SCB X vs. Silicon Craft Technology | SCB X vs. CENTRAL RETAIL P |
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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