Cotton Commodity Chance of Future Commodity Price Finishing Under 72.09
| CTUSX Commodity | 71.95 1.62 2.20% |
Cotton |
Cotton Target Price Odds to finish below 72.09
The tendency of Cotton 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 stay under 72.09 after 90 days |
| 71.95 | 90 days | 72.09 | about 72.25 |
Based on a normal probability distribution, the odds of Cotton to stay under 72.09 after 90 days from now is about 72.25 (This Cotton probability density function shows the probability of Cotton Commodity to fall within a particular range of prices over 90 days) . Probability of Cotton price to stay between its current price of 71.95 and 72.09 at the end of the 90-day period is roughly 2.97 .
Assuming the 90 days horizon Cotton has a beta of 0.33 suggesting as returns on the market go up, Cotton average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding Cotton will be expected to be much smaller as well. Additionally Cotton has a negative alpha, implying that the risk taken by holding this instrument is not justified. The company is significantly underperforming the Dow Jones Industrial. Cotton Price Density |
| Price |
Predictive Modules for Cotton
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Cotton. 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 Cotton'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.
Cotton Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. Cotton is not an exception. The market had few large corrections towards the Cotton'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 Cotton, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of Cotton within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | -0.0021 | |
β | Beta against Dow Jones | 0.33 | |
σ | Overall volatility | 1.61 | |
Ir | Information ratio | -0.07 |
Cotton Technical Analysis
Cotton's future price can be derived by breaking down and analyzing its technical indicators over time. Cotton Commodity technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Cotton. In general, you should focus on analyzing Cotton Commodity price patterns and their correlations with different microeconomic environments and drivers.
Cotton Predictive Forecast Models
Cotton's time-series forecasting models is one of many Cotton'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 Cotton'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 Cotton 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, Cotton's short interest history, or implied volatility extrapolated from Cotton options trading.