Correlation Between Goldsun Building and Basso Industry
Can any of the company-specific risk be diversified away by investing in both Goldsun Building and Basso Industry 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 Goldsun Building and Basso Industry into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Goldsun Building Materials and Basso Industry Corp, you can compare the effects of market volatilities on Goldsun Building and Basso Industry 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 Goldsun Building with a short position of Basso Industry. Check out your portfolio center. Please also check ongoing floating volatility patterns of Goldsun Building and Basso Industry.
Diversification Opportunities for Goldsun Building and Basso Industry
0.02 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Goldsun and Basso is 0.02. Overlapping area represents the amount of risk that can be diversified away by holding Goldsun Building Materials and Basso Industry Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Basso Industry Corp and Goldsun Building 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 Goldsun Building Materials are associated (or correlated) with Basso Industry. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Basso Industry Corp has no effect on the direction of Goldsun Building i.e., Goldsun Building and Basso Industry go up and down completely randomly.
Pair Corralation between Goldsun Building and Basso Industry
Assuming the 90 days trading horizon Goldsun Building Materials is expected to generate 1.46 times more return on investment than Basso Industry. However, Goldsun Building is 1.46 times more volatile than Basso Industry Corp. It trades about 0.01 of its potential returns per unit of risk. Basso Industry Corp is currently generating about -0.03 per unit of risk. If you would invest 4,450 in Goldsun Building Materials on October 10, 2024 and sell it today you would lose (100.00) from holding Goldsun Building Materials or give up 2.25% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Goldsun Building Materials vs. Basso Industry Corp
Performance |
Timeline |
Goldsun Building Mat |
Basso Industry Corp |
Goldsun Building and Basso Industry Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Goldsun Building and Basso Industry
The main advantage of trading using opposite Goldsun Building and Basso Industry positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Goldsun Building position performs unexpectedly, Basso Industry 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 Basso Industry will offset losses from the drop in Basso Industry's long position.Goldsun Building vs. Cathay Real Estate | Goldsun Building vs. Huaku Development Co | Goldsun Building vs. BES Engineering Co | Goldsun Building vs. Prince Housing Development |
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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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