Cotton Commodity Forecast - Polynomial Regression
| CTUSX Commodity | 71.65 0.03 0.04% |
Cotton |
Cotton Polynomial Regression Price Forecast For the 28th of November
Given 90 days horizon, the Polynomial Regression forecasted value of Cotton on the next trading day is expected to be 72.09 with a mean absolute deviation of 0.65, mean absolute percentage error of 0.69, and the sum of the absolute errors of 39.90.Please note that although there have been many attempts to predict Cotton Commodity prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Cotton's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Cotton Commodity Forecast Pattern
Cotton Forecasted Value
In the context of forecasting Cotton's Commodity value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. Cotton's downside and upside margins for the forecasting period are 70.89 and 73.29, respectively. We have considered Cotton's daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of Cotton commodity data series using in forecasting. Note that when a statistical model is used to represent Cotton commodity, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.| AIC | Akaike Information Criteria | 117.7385 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.6542 |
| MAPE | Mean absolute percentage error | 0.0092 |
| SAE | Sum of the absolute errors | 39.904 |
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.
Other Forecasting Options for Cotton
For every potential investor in Cotton, whether a beginner or expert, Cotton's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Cotton Commodity price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Cotton. Basic forecasting techniques help filter out the noise by identifying Cotton's price trends.View Cotton Related Equities
| Risk & Return | Correlation |
Cotton Technical and Predictive Analytics
The commodity market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Cotton's price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of Cotton's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Cotton Market Strength Events
Market strength indicators help investors to evaluate how Cotton commodity reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Cotton shares will generate the highest return on investment. By undertsting and applying Cotton commodity market strength indicators, traders can identify Cotton entry and exit signals to maximize returns.
Cotton Risk Indicators
The analysis of Cotton's basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Cotton's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting cotton commodity prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
| Mean Deviation | 0.9518 | |||
| Semi Deviation | 1.05 | |||
| Standard Deviation | 1.24 | |||
| Variance | 1.55 | |||
| Downside Variance | 1.21 | |||
| Semi Variance | 1.09 | |||
| Expected Short fall | (1.06) |
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.