USCF ETF Etf Forward View - Double Exponential Smoothing
| UDI Etf | USD 34.70 0.60 1.76% |
USCF Etf outlook is based on your current time horizon. We suggest always using this module together with an analysis of USCF ETF's historical fundamentals, such as revenue growth or operating cash flow patterns.
As of 7th of February 2026 the relative strength momentum indicator of USCF ETF's share price is below 20 . This usually implies that the etf is significantly oversold. The fundamental principle of the Relative Strength Index (RSI) is to quantify the velocity at which market participants are driving the price of a financial instrument upwards or downwards. Momentum 0
Sell Peaked
Oversold | Overbought |
Using USCF ETF hype-based prediction, you can estimate the value of USCF ETF Trust from the perspective of USCF ETF response to recently generated media hype and the effects of current headlines on its competitors.
The Double Exponential Smoothing forecasted value of USCF ETF Trust on the next trading day is expected to be 34.99 with a mean absolute deviation of 0.17 and the sum of the absolute errors of 10.41. USCF ETF after-hype prediction price | USD 34.77 |
There is no one specific way to measure market sentiment using hype analysis or a similar predictive technique. This prediction method should be used in combination with more fundamental and traditional techniques such as etf price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
Check out Historical Fundamental Analysis of USCF ETF to cross-verify your projections. USCF ETF Additional Predictive Modules
Most predictive techniques to examine USCF price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for USCF using various technical indicators. When you analyze USCF charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
USCF ETF Double Exponential Smoothing Price Forecast For the 8th of February
Given 90 days horizon, the Double Exponential Smoothing forecasted value of USCF ETF Trust on the next trading day is expected to be 34.99 with a mean absolute deviation of 0.17, mean absolute percentage error of 0.05, and the sum of the absolute errors of 10.41.Please note that although there have been many attempts to predict USCF Etf prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that USCF ETF's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
USCF ETF Etf Forecast Pattern
| Backtest USCF ETF | USCF ETF Price Prediction | Research Analysis |
USCF ETF Forecasted Value
In the context of forecasting USCF ETF's Etf 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. USCF ETF's downside and upside margins for the forecasting period are 34.35 and 35.63, respectively. We have considered USCF ETF'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 Double Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of USCF ETF etf data series using in forecasting. Note that when a statistical model is used to represent USCF ETF etf, 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 | Huge |
| Bias | Arithmetic mean of the errors | 0.0083 |
| MAD | Mean absolute deviation | 0.1735 |
| MAPE | Mean absolute percentage error | 0.0054 |
| SAE | Sum of the absolute errors | 10.4082 |
Predictive Modules for USCF ETF
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as USCF ETF Trust. Regardless of method or technology, however, to accurately forecast the etf market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the etf 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 USCF ETF'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.
USCF ETF After-Hype Price Density Analysis
As far as predicting the price of USCF ETF at your current risk attitude, this probability distribution graph shows the chance that the prediction will fall between or within a specific range. We use this chart to confirm that your returns on investing in USCF ETF or, for that matter, your successful expectations of its future price, cannot be replicated consistently. Please note, a large amount of money has been lost over the years by many investors who confused the symmetrical distributions of Etf prices, such as prices of USCF ETF, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
USCF ETF Estimiated After-Hype Price Volatility
In the context of predicting USCF ETF's etf value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on USCF ETF's historical news coverage. USCF ETF's after-hype downside and upside margins for the prediction period are 34.13 and 35.41, respectively. We have considered USCF ETF's daily market price in relation to the headlines to evaluate this method's predictive performance. Remember, however, there is no scientific proof or empirical evidence that news-based prediction models compare with traditional linear, nonlinear models or artificial intelligence models to provide accurate predictions consistently.
Current Value
USCF ETF is very steady at this time. Analysis and calculation of next after-hype price of USCF ETF Trust is based on 3 months time horizon.
USCF ETF Etf Price Outlook Analysis
Have you ever been surprised when a price of a ETF such as USCF ETF is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading USCF ETF backward and forwards among themselves. Have you ever observed a lot of a particular company's price movement is driven by press releases or news about the company that has nothing to do with actual earnings? Usually, hype to individual companies acts as price momentum. If not enough favorable publicity is forthcoming, the Etf price eventually runs out of speed. So, the rule of thumb here is that as long as this news hype has nothing to do with immediate earnings, you should pay more attention to it. If you see this tendency with USCF ETF, there might be something going there, and it might present an excellent short sale opportunity.
| Expected Return | Period Volatility | Hype Elasticity | Related Elasticity | News Density | Related Density | Expected Hype |
0.21 | 0.64 | 0.07 | 0.01 | 2 Events / Month | 4 Events / Month | In a few days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
34.70 | 34.77 | 0.20 |
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USCF ETF Hype Timeline
On the 7th of February USCF ETF Trust is traded for 34.70. The entity has historical hype elasticity of 0.07, and average elasticity to hype of competition of -0.01. USCF is forecasted to increase in value after the next headline, with the price projected to jump to 34.77 or above. The average volatility of media hype impact on the company the price is about 193.94%. The price growth on the next news is projected to be 0.2%, whereas the daily expected return is at this time at 0.21%. The volatility of related hype on USCF ETF is about 976.27%, with the expected price after the next announcement by competition of 34.69. Considering the 90-day investment horizon the next forecasted press release will be in a few days. Check out Historical Fundamental Analysis of USCF ETF to cross-verify your projections.USCF ETF Related Hype Analysis
Having access to credible news sources related to USCF ETF's direct competition is more important than ever and may enhance your ability to predict USCF ETF's future price movements. Getting to know how USCF ETF's peers react to changing market sentiment, related social signals, and mainstream news is a great way to find investing opportunities and time the market. The summary table below summarizes the essential lagging indicators that can help you analyze how USCF ETF may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| CGXU | Capital Group International | 0.13 | 8 per month | 1.04 | 0.02 | 1.59 | (2.00) | 4.37 | |
| DFLV | Dimensional ETF Trust | (0.09) | 5 per month | 0.42 | 0.13 | 1.59 | (1.18) | 3.09 | |
| JHMM | John Hancock Multifactor | 0.16 | 4 per month | 0.67 | 0.07 | 1.72 | (1.44) | 3.56 | |
| FENI | Fidelity Covington Trust | 0.01 | 5 per month | 0.62 | 0.11 | 1.26 | (1.19) | 3.25 | |
| VMIAX | Vanguard Materials Index | 0.23 | 1 per month | 0.73 | 0.19 | 2.20 | (1.63) | 5.22 | |
| BBEU | JPMorgan BetaBuilders Europe | 0.74 | 2 per month | 0.61 | 0.09 | 1.30 | (1.25) | 3.13 | |
| DXJ | WisdomTree Japan Hedged | (0.72) | 2 per month | 0.60 | 0.19 | 2.13 | (1.56) | 4.81 | |
| IHI | iShares Medical Devices | (0.1) | 5 per month | 0.00 | (0.16) | 1.42 | (1.31) | 4.45 | |
| SLYV | SPDR SP 600 | (0.95) | 8 per month | 0.79 | 0.11 | 2.46 | (1.81) | 5.07 |
Other Forecasting Options for USCF ETF
For every potential investor in USCF, whether a beginner or expert, USCF ETF's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. USCF Etf price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in USCF. Basic forecasting techniques help filter out the noise by identifying USCF ETF's price trends.USCF ETF Related Equities
One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with USCF ETF etf to make a market-neutral strategy. Peer analysis of USCF ETF could also be used in its relative valuation, which is a method of valuing USCF ETF by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
USCF ETF Market Strength Events
Market strength indicators help investors to evaluate how USCF ETF etf reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading USCF ETF shares will generate the highest return on investment. By undertsting and applying USCF ETF etf market strength indicators, traders can identify USCF ETF Trust entry and exit signals to maximize returns.
USCF ETF Risk Indicators
The analysis of USCF ETF'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 USCF ETF's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting uscf etf 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 | 0.4988 | |||
| Standard Deviation | 0.6209 | |||
| Variance | 0.3856 | |||
| Downside Variance | 0.256 | |||
| Semi Variance | (0.02) | |||
| Expected Short fall | (0.64) |
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.
Story Coverage note for USCF ETF
The number of cover stories for USCF ETF depends on current market conditions and USCF ETF's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that USCF ETF is classified under. However, while its typical story may have numerous social followers, the rapid visibility can also attract short-sellers, who usually are skeptical about USCF ETF's long-term prospects. So, having above-average coverage will typically attract above-average short interest, leading to significant price volatility.
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Check out Historical Fundamental Analysis of USCF ETF to cross-verify your projections. You can also try the ETFs module to find actively traded Exchange Traded Funds (ETF) from around the world.
Understanding USCF ETF Trust requires distinguishing between market price and book value, where the latter reflects USCF's accounting equity. The concept of intrinsic value - what USCF ETF's is actually worth based on fundamentals - guides informed investors toward better entry and exit points. Analysts utilize numerous techniques to assess fundamental value, seeking to purchase shares when trading prices fall beneath estimated intrinsic worth. Market sentiment, economic cycles, and investor behavior can push USCF ETF's price substantially above or below its fundamental value.
Please note, there is a significant difference between USCF ETF's value and its price as these two are different measures arrived at by different means. Investors typically determine if USCF ETF is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. In contrast, USCF ETF's trading price reflects the actual exchange value where willing buyers and sellers reach mutual agreement.