30 Day Fed Commodity Chance of Future Commodity Price Finishing Over 95.48
| ZQUSD Commodity | 95.48 0.01 0.01% |
ZQUSD |
30 Day Target Price Odds to finish over 95.48
The tendency of ZQUSD Commodity price to converge on an average value over time is a known aspect in finance that investors have used since the beginning of the stock market for forecasting. However, many studies suggest that some traded equity instruments are consistently mispriced before traders' demand and supply correct the spread. One possible conclusion to this anomaly is that these stocks have additional risk, for which investors demand compensation in the form of extra returns.
| Current Price | Horizon | Target Price | Odds to move above the current price in 90 days |
| 95.48 | 90 days | 95.48 | about 18.82 |
Based on a normal probability distribution, the odds of 30 Day to move above the current price in 90 days from now is about 18.82 (This 30 Day Fed probability density function shows the probability of ZQUSD Commodity to fall within a particular range of prices over 90 days) .
Assuming the 90 days horizon 30 Day Fed has a beta of -0.0198. This usually means as returns on the benchmark increase, returns on holding 30 Day are expected to decrease at a much lower rate. During a bear market, however, 30 Day Fed is likely to outperform the market. Additionally 30 Day Fed has an alpha of 0.0053, implying that it can generate a 0.005252 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta). 30 Day Price Density |
| Price |
Predictive Modules for 30 Day
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as 30 Day Fed. Regardless of method or technology, however, to accurately forecast the commodity market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the commodity market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of 30 Day's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
30 Day Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. 30 Day is not an exception. The market had few large corrections towards the 30 Day's value, including both sudden drops in prices as well as massive rallies. These swings have made and broken many portfolios. An investor can limit the violent swings in their portfolio by implementing a hedging strategy designed to limit downside losses. If you hold 30 Day Fed, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of 30 Day within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | 0.01 | |
β | Beta against Dow Jones | -0.02 | |
σ | Overall volatility | 0.26 | |
Ir | Information ratio | -1.04 |
30 Day Technical Analysis
30 Day's future price can be derived by breaking down and analyzing its technical indicators over time. ZQUSD Commodity technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of 30 Day Fed. In general, you should focus on analyzing ZQUSD Commodity price patterns and their correlations with different microeconomic environments and drivers.
30 Day Predictive Forecast Models
30 Day's time-series forecasting models is one of many 30 Day's commodity analysis techniques aimed to predict future share value based on previously observed values. Time-series forecasting models are widely used for non-stationary data. Non-stationary data are called the data whose statistical properties, e.g., the mean and standard deviation, are not constant over time, but instead, these metrics vary over time. This non-stationary 30 Day's historical data is usually called time series. Some empirical experimentation suggests that the statistical forecasting models outperform the models based exclusively on fundamental analysis to predict the direction of the commodity market movement and maximize returns from investment trading.
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards 30 Day in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, 30 Day's short interest history, or implied volatility extrapolated from 30 Day options trading.