Correlation Between Pitney Bowes and Verisk Analytics

Specify exactly 2 symbols:
Can any of the company-specific risk be diversified away by investing in both Pitney Bowes and Verisk Analytics 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 Pitney Bowes and Verisk Analytics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pitney Bowes and Verisk Analytics, you can compare the effects of market volatilities on Pitney Bowes and Verisk Analytics 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 Pitney Bowes with a short position of Verisk Analytics. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pitney Bowes and Verisk Analytics.

Diversification Opportunities for Pitney Bowes and Verisk Analytics

-0.18
  Correlation Coefficient

Good diversification

The 3 months correlation between Pitney and Verisk is -0.18. Overlapping area represents the amount of risk that can be diversified away by holding Pitney Bowes and Verisk Analytics in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Verisk Analytics and Pitney Bowes 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 Pitney Bowes are associated (or correlated) with Verisk Analytics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Verisk Analytics has no effect on the direction of Pitney Bowes i.e., Pitney Bowes and Verisk Analytics go up and down completely randomly.

Pair Corralation between Pitney Bowes and Verisk Analytics

Considering the 90-day investment horizon Pitney Bowes is expected to under-perform the Verisk Analytics. In addition to that, Pitney Bowes is 2.3 times more volatile than Verisk Analytics. It trades about -0.1 of its total potential returns per unit of risk. Verisk Analytics is currently generating about -0.03 per unit of volatility. If you would invest  19,881  in Verisk Analytics on June 29, 2022 and sell it today you would lose (2,778)  from holding Verisk Analytics or give up 13.97% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthInsignificant
Accuracy100.0%
ValuesDaily Returns

Pitney Bowes  vs.  Verisk Analytics

 Performance (%) 
       Timeline  
Pitney Bowes 
Pitney Performance
0 of 100
Over the last 90 days Pitney Bowes has generated negative risk-adjusted returns adding no value to investors with long positions. Despite abnormal performance in the last few months, the Stock's fundamental drivers remain nearly stable which may send shares a bit higher in October 2022. The current disturbance may also be a sign of long-run up-swing for the company stockholders.

Pitney Price Channel

Verisk Analytics 
Verisk Performance
1 of 100
Compared to the overall equity markets, risk-adjusted returns on investments in Verisk Analytics are ranked lower than 1 (%) of all global equities and portfolios over the last 90 days. Despite fairly strong basic indicators, Verisk Analytics is not utilizing all of its potentials. The latest stock price confusion, may contribute to short-horizon losses for the traders.

Verisk Price Channel

Pitney Bowes and Verisk Analytics Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Pitney Bowes and Verisk Analytics

The main advantage of trading using opposite Pitney Bowes and Verisk Analytics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pitney Bowes position performs unexpectedly, Verisk Analytics 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 Verisk Analytics will offset losses from the drop in Verisk Analytics' long position.
Pitney Bowes vs. Clearwater Paper Corp
The idea behind Pitney Bowes and Verisk Analytics 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.
Verisk Analytics vs. Clearwater Paper Corp
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 Equity Search module to search for actively traded equities including funds and ETFs from over 30 global markets.

Other Complementary Tools

Performance Analysis
Check effects of mean-variance optimization against your current asset allocation
Go
Idea Breakdown
Analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes
Go
Transaction History
View history of all your transactions and understand their impact on performance
Go
Portfolio Volatility
Check portfolio volatility and analyze historical return density to properly model market risk
Go
Pair Correlation
Compare performance and examine fundamental relationship between any two equity instruments
Go
Options Analysis
Analyze and evaluate options and option chains as a potential hedge for your portfolios
Go
Portfolio Volatility
Check portfolio volatility and analyze historical return density to properly model market risk
Go
Watchlist Optimization
Optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm
Go
Portfolio Suggestion
Get suggestions outside of your existing asset allocation including your own model portfolios
Go