Correlation Between Quant and Ethereum

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

Diversification Opportunities for Quant and Ethereum

0.75
  Correlation Coefficient

Poor diversification

The 3 months correlation between Quant and Ethereum is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding Quant and Ethereum in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ethereum and Quant 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 Quant are associated (or correlated) with Ethereum. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ethereum has no effect on the direction of Quant i.e., Quant and Ethereum go up and down completely randomly.

Pair Corralation between Quant and Ethereum

Assuming the 90 days trading horizon Quant is expected to under-perform the Ethereum. In addition to that, Quant is 1.35 times more volatile than Ethereum. It trades about -0.21 of its total potential returns per unit of risk. Ethereum is currently generating about -0.14 per unit of volatility. If you would invest  349,977  in Ethereum on January 26, 2024 and sell it today you would lose (37,654) from holding Ethereum or give up 10.76% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthSignificant
Accuracy95.65%
ValuesDaily Returns

Quant  vs.  Ethereum

 Performance 
       Timeline  
Quant 

Risk-Adjusted Performance

1 of 100

 
Weak
 
Strong
Weak
Compared to the overall equity markets, risk-adjusted returns on investments in Quant are ranked lower than 1 (%) of all global equities and portfolios over the last 90 days. In spite of rather sound basic indicators, Quant is not utilizing all of its potentials. The latest stock price tumult, may contribute to shorter-term losses for the shareholders.
Ethereum 

Risk-Adjusted Performance

11 of 100

 
Weak
 
Strong
Good
Compared to the overall equity markets, risk-adjusted returns on investments in Ethereum are ranked lower than 11 (%) of all global equities and portfolios over the last 90 days. In spite of rather unsteady technical indicators, Ethereum exhibited solid returns over the last few months and may actually be approaching a breakup point.

Quant and Ethereum Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Quant and Ethereum

The main advantage of trading using opposite Quant and Ethereum positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Quant position performs unexpectedly, Ethereum 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 Ethereum will offset losses from the drop in Ethereum's long position.
The idea behind Quant and Ethereum 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 Investing Opportunities module to build portfolios using our predefined set of ideas and optimize them against your investing preferences.

Other Complementary Tools

Idea Optimizer
Use advanced portfolio builder with pre-computed micro ideas to build optimal portfolio
CEOs Directory
Screen CEOs from public companies around the world
Earnings Calls
Check upcoming earnings announcements updated hourly across public exchanges
Piotroski F Score
Get Piotroski F Score based on the binary analysis strategy of nine different fundamentals
Commodity Directory
Find actively traded commodities issued by global exchanges
Correlation Analysis
Reduce portfolio risk simply by holding instruments which are not perfectly correlated
Idea Breakdown
Analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes
Efficient Frontier
Plot and analyze your portfolio and positions against risk-return landscape of the market.
Bollinger Bands
Use Bollinger Bands indicator to analyze target price for a given investing horizon
Pattern Recognition
Use different Pattern Recognition models to time the market across multiple global exchanges
Pair Correlation
Compare performance and examine fundamental relationship between any two equity instruments
Instant Ratings
Determine any equity ratings based on digital recommendations. Macroaxis instant equity ratings are based on combination of fundamental analysis and risk-adjusted market performance
Portfolio Volatility
Check portfolio volatility and analyze historical return density to properly model market risk