Goldman Sachs Alpha and Beta Analysis Overview

GDCCX -- USA Fund  

USD 6.34  0.00  0.00%

This module allows you to check different measures of market premium for Goldman Sachs Dynamic Cmdty Strat C as well as systematic risk associated with investing in Goldman Sachs over a specified time horizon. Please also check Risk vs Return Analysis.
Horizon     30 Days    Login   to change
Symbol
Run Premiums

Goldman Sachs Market Premiums

α0.00   β0.00
30 days against DJI

Goldman Sachs Fundamentals

    
 Better Than Average     
    
 Worse Than Average Compare Goldman Sachs to competition

Goldman Sachs Fundamental Vs Peers

FundamentalsGoldman SachsPeer Average
One Year Return(24.56) % 2.30 %
Net Asset24.97 M1.37 B
Minimum Initial Investment1 K8.09 M
Cash Position Weight80.80 % 14.48 %

Goldman Sachs Opportunities

Goldman Sachs Return and Market Media

The median price of Goldman Sachs for the period between Wed, Oct 17, 2018 and Fri, Nov 16, 2018 is 6.34 with a coefficient of variation of 40.67. The daily time series for the period is distributed with a sample standard deviation of 2.23, arithmetic mean of 5.48, and mean deviation of 1.49. The Fund did not receive any noticable media coverage during the period.
 Price Growth (%)  
      Timeline 

Current Sentiment - GDCCX

Goldman Sachs Dynamic Investor Sentiment

Macroaxis portfolio users are indifferent in their judgment towards investing in Goldman Sachs Dynamic Cmdty Strat C. What is your judgment towards investing in Goldman Sachs Dynamic Cmdty Strat C? Are you bullish or bearish?
Bullish
Bearish
50% Bullish
50% Bearish
Skip

Build Diversified Portfolios

Align your risk with return expectations

Fix your portfolio
By capturing your risk tolerance and investment horizon Macroaxis technology of instant portfolio optimization will compute exactly how much risk is acceptable for your desired return expectations
Please also check Risk vs Return Analysis. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.
Search macroaxis.com