Lumber Futures Commodity Forecast - Naive Prediction
| LBUSD Commodity | 589.00 0.50 0.09% |
Lumber |
Lumber Futures Naive Prediction Price Forecast For the 30th of November
Given 90 days horizon, the Naive Prediction forecasted value of Lumber Futures on the next trading day is expected to be 561.00 with a mean absolute deviation of 6.44, mean absolute percentage error of 71.93, and the sum of the absolute errors of 392.60.Please note that although there have been many attempts to predict Lumber 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 Lumber Futures' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Lumber Futures Commodity Forecast Pattern
Lumber Futures Forecasted Value
In the context of forecasting Lumber Futures' 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. Lumber Futures' downside and upside margins for the forecasting period are 559.24 and 562.77, respectively. We have considered Lumber Futures' 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 Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Lumber Futures commodity data series using in forecasting. Note that when a statistical model is used to represent Lumber Futures 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 | 122.3862 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 6.4361 |
| MAPE | Mean absolute percentage error | 0.0116 |
| SAE | Sum of the absolute errors | 392.6015 |
Predictive Modules for Lumber Futures
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Lumber Futures. 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 Lumber Futures' 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 Lumber Futures
For every potential investor in Lumber, whether a beginner or expert, Lumber Futures' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Lumber Commodity price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Lumber. Basic forecasting techniques help filter out the noise by identifying Lumber Futures' price trends.Lumber Futures 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 Lumber Futures, pairing it with other commodities or financial instruments to create a balanced, market-neutral setup.
| Risk & Return | Correlation |
Lumber Futures 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 Lumber Futures' 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 Lumber Futures' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Lumber Futures Market Strength Events
Market strength indicators help investors to evaluate how Lumber Futures commodity reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Lumber Futures shares will generate the highest return on investment. By undertsting and applying Lumber Futures commodity market strength indicators, traders can identify Lumber Futures entry and exit signals to maximize returns.
Lumber Futures Risk Indicators
The analysis of Lumber Futures' 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 Lumber Futures' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting lumber 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 | 1.26 | |||
| Semi Deviation | 1.03 | |||
| Standard Deviation | 1.76 | |||
| Variance | 3.08 | |||
| Downside Variance | 1.52 | |||
| Semi Variance | 1.06 | |||
| Expected Short fall | (1.49) |
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.