Correlation Between PING and TNB

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Can any of the company-specific risk be diversified away by investing in both PING and TNB 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 PING and TNB into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between PING and TNB, you can compare the effects of market volatilities on PING and TNB 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 PING with a short position of TNB. Check out your portfolio center. Please also check ongoing floating volatility patterns of PING and TNB.

Diversification Opportunities for PING and TNB

0.55
  Correlation Coefficient

Very weak diversification

The 3 months correlation between PING and TNB is 0.55. Overlapping area represents the amount of risk that can be diversified away by holding PING and TNB in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on TNB and PING 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 PING are associated (or correlated) with TNB. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of TNB has no effect on the direction of PING i.e., PING and TNB go up and down completely randomly.

Pair Corralation between PING and TNB

If you would invest  0.01  in TNB on August 30, 2024 and sell it today you would earn a total of  0.00  from holding TNB or generate 64.15% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthWeak
Accuracy1.56%
ValuesDaily Returns

PING  vs.  TNB

 Performance 
       Timeline  
PING 

Risk-Adjusted Performance

0 of 100

 
Weak
 
Strong
Very Weak
Over the last 90 days PING has generated negative risk-adjusted returns adding no value to investors with long positions. In spite of rather sound fundamental indicators, PING is not utilizing all of its potentials. The latest stock price tumult, may contribute to shorter-term losses for the shareholders.
TNB 

Risk-Adjusted Performance

19 of 100

 
Weak
 
Strong
Solid
Compared to the overall equity markets, risk-adjusted returns on investments in TNB are ranked lower than 19 (%) of all global equities and portfolios over the last 90 days. In spite of rather unsteady fundamental drivers, TNB exhibited solid returns over the last few months and may actually be approaching a breakup point.

PING and TNB Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with PING and TNB

The main advantage of trading using opposite PING and TNB positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if PING position performs unexpectedly, TNB 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 TNB will offset losses from the drop in TNB's long position.
The idea behind PING and TNB pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.

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