Coffee Commodity Forecast - Polynomial Regression
| KCUSX Commodity | 302.90 7.20 2.43% |
Coffee |
Coffee Polynomial Regression Price Forecast For the 24th of November
Given 90 days horizon, the Polynomial Regression forecasted value of Coffee on the next trading day is expected to be 313.03 with a mean absolute deviation of 4.96, mean absolute percentage error of 37.23, and the sum of the absolute errors of 307.37.Please note that although there have been many attempts to predict Coffee 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 Coffee's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Coffee Commodity Forecast Pattern
Coffee Forecasted Value
In the context of forecasting Coffee'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. Coffee's downside and upside margins for the forecasting period are 310.83 and 315.23, respectively. We have considered Coffee'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 Coffee commodity data series using in forecasting. Note that when a statistical model is used to represent Coffee 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 | 123.5655 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 4.9576 |
| MAPE | Mean absolute percentage error | 0.0193 |
| SAE | Sum of the absolute errors | 307.3699 |
Predictive Modules for Coffee
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Coffee. 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 Coffee'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 Coffee
For every potential investor in Coffee, whether a beginner or expert, Coffee's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Coffee Commodity price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Coffee. Basic forecasting techniques help filter out the noise by identifying Coffee's price trends.Coffee Related Commodities
One prevalent trading approach among algorithmic traders in the commodities sector involves employing market-neutral strategies, wherein each trade is designed to hedge away specific risks. Given that this approach necessitates two distinct transactions, if one position underperforms unexpectedly, the other can potentially offset some of the losses. This method can be applied to commodities such as Coffee, pairing it with other commodities or financial instruments to create a balanced, market-neutral setup.
| Risk & Return | Correlation |
Coffee 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 Coffee'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 Coffee's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Coffee Market Strength Events
Market strength indicators help investors to evaluate how Coffee commodity reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Coffee shares will generate the highest return on investment. By undertsting and applying Coffee commodity market strength indicators, traders can identify Coffee entry and exit signals to maximize returns.
Coffee Risk Indicators
The analysis of Coffee'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 Coffee's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting coffee 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 | 1.73 | |||
| Semi Deviation | 2.04 | |||
| Standard Deviation | 2.18 | |||
| Variance | 4.77 | |||
| Downside Variance | 4.76 | |||
| Semi Variance | 4.15 | |||
| Expected Short fall | (1.82) |
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.