Clean Energy Stock Forward View - Simple Regression
| CLNE Stock | USD 2.58 0.14 5.15% |
Clean Stock outlook is based on your current time horizon. Investors can use this forecasting interface to forecast Clean Energy stock prices and determine the direction of Clean Energy Fuels's future trends based on various well-known forecasting models. We suggest always using this module together with an analysis of Clean Energy's historical fundamentals, such as revenue growth or operating cash flow patterns.
At the present time the relative strength momentum indicator of Clean Energy's share price is below 20 suggesting that the stock 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 Clean Energy hype-based prediction, you can estimate the value of Clean Energy Fuels from the perspective of Clean Energy response to recently generated media hype and the effects of current headlines on its competitors.
The Simple Regression forecasted value of Clean Energy Fuels on the next trading day is expected to be 2.33 with a mean absolute deviation of 0.08 and the sum of the absolute errors of 5.02. Clean Energy after-hype prediction price | USD 2.59 |
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 stock price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
Check out Historical Fundamental Analysis of Clean Energy to cross-verify your projections. Clean Energy Additional Predictive Modules
Most predictive techniques to examine Clean price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for Clean using various technical indicators. When you analyze Clean 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 |
Clean Energy Simple Regression Price Forecast For the 11th of February 2026
Given 90 days horizon, the Simple Regression forecasted value of Clean Energy Fuels on the next trading day is expected to be 2.33 with a mean absolute deviation of 0.08, mean absolute percentage error of 0.01, and the sum of the absolute errors of 5.02.Please note that although there have been many attempts to predict Clean Stock 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 Clean Energy's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Clean Energy Stock Forecast Pattern
| Backtest Clean Energy | Clean Energy Price Prediction | Research Analysis |
Clean Energy Forecasted Value
In the context of forecasting Clean Energy's Stock 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. Clean Energy's downside and upside margins for the forecasting period are 0.03 and 5.00, respectively. We have considered Clean Energy'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 Clean Energy stock data series using in forecasting. Note that when a statistical model is used to represent Clean Energy stock, 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 | 113.7549 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0823 |
| MAPE | Mean absolute percentage error | 0.0357 |
| SAE | Sum of the absolute errors | 5.0223 |
Predictive Modules for Clean Energy
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Clean Energy Fuels. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock 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.Clean Energy After-Hype Price Density Analysis
As far as predicting the price of Clean Energy 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 Clean Energy 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 Stock prices, such as prices of Clean Energy, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
Clean Energy Estimiated After-Hype Price Volatility
In the context of predicting Clean Energy's stock value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on Clean Energy's historical news coverage. Clean Energy's after-hype downside and upside margins for the prediction period are 0.13 and 5.25, respectively. We have considered Clean Energy'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
Clean Energy is relatively risky at this time. Analysis and calculation of next after-hype price of Clean Energy Fuels is based on 3 months time horizon.
Clean Energy Stock Price Outlook Analysis
Have you ever been surprised when a price of a Company such as Clean Energy is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading Clean Energy 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 Stock 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 Clean Energy, 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 |
0.26 | 2.66 | 0.00 | 0.02 | 0 Events / Month | 1 Events / Month | In 5 to 10 days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
2.58 | 2.59 | 0.39 |
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Clean Energy Hype Timeline
Clean Energy Fuels is currently traded for 2.58. The entity stock is not elastic to its hype. The average elasticity to hype of competition is 0.02. Clean is estimated to increase in value after the next headline, with the price projected to jump to 2.59 or above. The average volatility of media hype impact on the company the price is insignificant. The price growth on the next news is projected to be 0.39%, whereas the daily expected return is currently at 0.26%. The volatility of related hype on Clean Energy is about 3520.59%, with the expected price after the next announcement by competition of 2.60. The company reported the previous year's revenue of 415.87 M. Net Loss for the year was (30.63 M) with profit before overhead, payroll, taxes, and interest of 117.77 M. Given the investment horizon of 90 days the next estimated press release will be in 5 to 10 days. Check out Historical Fundamental Analysis of Clean Energy to cross-verify your projections.Clean Energy Related Hype Analysis
Having access to credible news sources related to Clean Energy's direct competition is more important than ever and may enhance your ability to predict Clean Energy's future price movements. Getting to know how Clean Energy'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 Clean Energy may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| VTLE | Vital Energy | 0.00 | 0 per month | 1.45 | 0.05 | 4.56 | (3.67) | 7.26 | |
| NGL | NGL Energy Partners | 0.81 | 10 per month | 2.40 | 0.15 | 6.60 | (3.58) | 17.07 | |
| KOS | Kosmos Energy | 0.00 | 0 per month | 4.92 | 0.04 | 11.18 | (8.00) | 35.42 | |
| SOC | Sable Offshore Corp | (0.13) | 9 per month | 8.89 | 0.09 | 20.50 | (14.79) | 72.14 | |
| REPX | Riley Exploration Permian | 0.00 | 0 per month | 2.32 | 0.02 | 4.25 | (3.52) | 9.75 | |
| CAPL | Crossamerica Partners LP | 0.00 | 0 per month | 0.84 | 0.06 | 1.44 | (1.28) | 4.34 | |
| GRNT | Granite Ridge Resources | 0.00 | 0 per month | 2.38 | (0.02) | 3.44 | (4.10) | 8.29 | |
| URG | Ur Energy | 0.00 | 0 per month | 3.95 | 0.09 | 5.83 | (7.37) | 17.89 | |
| TNP | Tsakos Energy Navigation | 0.00 | 0 per month | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Other Forecasting Options for Clean Energy
For every potential investor in Clean, whether a beginner or expert, Clean Energy's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Clean Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Clean. Basic forecasting techniques help filter out the noise by identifying Clean Energy's price trends.Clean Energy 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 Clean Energy stock to make a market-neutral strategy. Peer analysis of Clean Energy could also be used in its relative valuation, which is a method of valuing Clean Energy by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Clean Energy Market Strength Events
Market strength indicators help investors to evaluate how Clean Energy stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Clean Energy shares will generate the highest return on investment. By undertsting and applying Clean Energy stock market strength indicators, traders can identify Clean Energy Fuels entry and exit signals to maximize returns.
| Accumulation Distribution | 60681.92 | |||
| Daily Balance Of Power | (1.00) | |||
| Rate Of Daily Change | 0.95 | |||
| Day Median Price | 2.65 | |||
| Day Typical Price | 2.63 | |||
| Price Action Indicator | (0.14) | |||
| Period Momentum Indicator | (0.14) |
Clean Energy Risk Indicators
The analysis of Clean Energy'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 Clean Energy's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting clean stock 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 | 2.03 | |||
| Semi Deviation | 2.28 | |||
| Standard Deviation | 2.66 | |||
| Variance | 7.09 | |||
| Downside Variance | 7.15 | |||
| Semi Variance | 5.21 | |||
| Expected Short fall | (2.32) |
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 Clean Energy
The number of cover stories for Clean Energy depends on current market conditions and Clean Energy's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that Clean Energy 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 Clean Energy'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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Clean Energy Short Properties
Clean Energy's future price predictability will typically decrease when Clean Energy's long traders begin to feel the short-sellers pressure to drive the price lower. The predictive aspect of Clean Energy Fuels often depends not only on the future outlook of the potential Clean Energy's investors but also on the ongoing dynamics between investors with different trading styles. Because the market risk indicators may have small false signals, it is better to identify suitable times to hedge a portfolio using different long/short signals. Clean Energy's indicators that are reflective of the short sentiment are summarized in the table below.
| Common Stock Shares Outstanding | 223.3 M | |
| Cash And Short Term Investments | 217.5 M |
Check out Historical Fundamental Analysis of Clean Energy to cross-verify your projections. You can also try the Correlation Analysis module to reduce portfolio risk simply by holding instruments which are not perfectly correlated.
Will Oil & Gas Refining & Marketing sector continue expanding? Could Clean diversify its offerings? Factors like these will boost the valuation of Clean Energy. Projected growth potential of Clean fundamentally drives upward valuation adjustments. Accurate valuation requires analyzing both current fundamentals and future growth trajectories. Every Clean Energy data point contributes insight, yet successful analysis hinges on identifying the most consequential variables.
Understanding Clean Energy Fuels requires distinguishing between market price and book value, where the latter reflects Clean's accounting equity. The concept of intrinsic value - what Clean Energy's is actually worth based on fundamentals - guides informed investors toward better entry and exit points. Seasoned market participants apply comprehensive analytical frameworks to derive fundamental worth and identify mispriced opportunities. Market sentiment, economic cycles, and investor behavior can push Clean Energy's price substantially above or below its fundamental value.
It's important to distinguish between Clean Energy's intrinsic value and market price, which are calculated using different methodologies. Investment decisions regarding Clean Energy should consider multiple factors including financial performance, growth metrics, competitive position, and professional analysis. In contrast, Clean Energy's trading price reflects the actual exchange value where willing buyers and sellers reach mutual agreement.