Correlation Between TG Therapeutics and FEC Resources
Can any of the company-specific risk be diversified away by investing in both TG Therapeutics and FEC Resources 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 TG Therapeutics and FEC Resources into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between TG Therapeutics and FEC Resources, you can compare the effects of market volatilities on TG Therapeutics and FEC Resources 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 TG Therapeutics with a short position of FEC Resources. Check out your portfolio center. Please also check ongoing floating volatility patterns of TG Therapeutics and FEC Resources.
Diversification Opportunities for TG Therapeutics and FEC Resources
-0.6 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between TGTX and FEC is -0.6. Overlapping area represents the amount of risk that can be diversified away by holding TG Therapeutics and FEC Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FEC Resources and TG Therapeutics 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 TG Therapeutics are associated (or correlated) with FEC Resources. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FEC Resources has no effect on the direction of TG Therapeutics i.e., TG Therapeutics and FEC Resources go up and down completely randomly.
Pair Corralation between TG Therapeutics and FEC Resources
Given the investment horizon of 90 days TG Therapeutics is expected to under-perform the FEC Resources. But the stock apears to be less risky and, when comparing its historical volatility, TG Therapeutics is 3.4 times less risky than FEC Resources. The stock trades about -0.09 of its potential returns per unit of risk. The FEC Resources is currently generating about 0.15 of returns per unit of risk over similar time horizon. If you would invest 0.21 in FEC Resources on September 24, 2024 and sell it today you would earn a total of 0.06 from holding FEC Resources or generate 28.57% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 95.24% |
Values | Daily Returns |
TG Therapeutics vs. FEC Resources
Performance |
Timeline |
TG Therapeutics |
FEC Resources |
TG Therapeutics and FEC Resources Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with TG Therapeutics and FEC Resources
The main advantage of trading using opposite TG Therapeutics and FEC Resources positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if TG Therapeutics position performs unexpectedly, FEC Resources 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 FEC Resources will offset losses from the drop in FEC Resources' long position.TG Therapeutics vs. Fate Therapeutics | TG Therapeutics vs. Sana Biotechnology | TG Therapeutics vs. Caribou Biosciences | TG Therapeutics vs. Arcus Biosciences |
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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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