30 Day Commodity Forecast - Simple Regression
| ZQUSD Commodity | 95.49 0.02 0.02% |
ZQUSD |
30 Day Simple Regression Price Forecast For the 28th of November
Given 90 days horizon, the Simple Regression forecasted value of 30 Day Fed on the next trading day is expected to be 95.62 with a mean absolute deviation of 0.08, mean absolute percentage error of 0.01, and the sum of the absolute errors of 4.71.Please note that although there have been many attempts to predict ZQUSD 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 30 Day's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
30 Day Commodity Forecast Pattern
30 Day Forecasted Value
In the context of forecasting 30 Day'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. 30 Day's downside and upside margins for the forecasting period are 95.51 and 95.73, respectively. We have considered 30 Day'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 Simple Regression forecasting method's relative quality and the estimations of the prediction error of 30 Day commodity data series using in forecasting. Note that when a statistical model is used to represent 30 Day 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 | 113.6061 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0771 |
| MAPE | Mean absolute percentage error | 8.0E-4 |
| SAE | Sum of the absolute errors | 4.7056 |
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.
Other Forecasting Options for 30 Day
For every potential investor in ZQUSD, whether a beginner or expert, 30 Day's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. ZQUSD Commodity price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in ZQUSD. Basic forecasting techniques help filter out the noise by identifying 30 Day's price trends.30 Day 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 30 Day, pairing it with other commodities or financial instruments to create a balanced, market-neutral setup.
| Risk & Return | Correlation |
30 Day Fed 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 30 Day'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 30 Day's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
30 Day Market Strength Events
Market strength indicators help investors to evaluate how 30 Day commodity reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading 30 Day shares will generate the highest return on investment. By undertsting and applying 30 Day commodity market strength indicators, traders can identify 30 Day Fed entry and exit signals to maximize returns.
30 Day Risk Indicators
The analysis of 30 Day'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 30 Day's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting zqusd 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 | 0.0436 | |||
| Standard Deviation | 0.1084 | |||
| Variance | 0.0118 | |||
| Downside Variance | 0.0117 | |||
| Semi Variance | (0.01) | |||
| Expected Short fall | (0.09) |
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.