Clean Energy (Germany) Price Prediction
| WIQ Stock | EUR 2.46 0.06 2.50% |
Oversold Vs Overbought
47
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 fear of missing out, i.e., FOMO, can cause potential investors in Clean Energy to buy its stock at a price that has no basis in reality. In that case, they are not buying Clean because the equity is a good investment, but because they need to do something to avoid the feeling of missing out. On the other hand, investors will often sell stocks at prices well below their value during bear markets because they need to stop feeling the pain of losing money.
Clean Energy after-hype prediction price | EUR 2.46 |
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.
Clean |
Clean Energy After-Hype Price Prediction 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 0.12 and 6.34, 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 outperform traditional linear, nonlinear models or artificial intelligence models to provide accurate predictions consistently.
Current Value
Clean Energy is very 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 Prediction 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 |
0.07 | 3.87 | 0.00 | 0.00 | 0 Events / Month | 0 Events / Month | Any time |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | ||
2.46 | 2.46 | 0.00 |
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Clean Energy Hype Timeline
Clean Energy Fuels is at this time traded for 2.46on Frankfurt Exchange of Germany. The entity stock is not elastic to its hype. The average elasticity to hype of competition is 0.0. Clean is estimated not to react to the next headline, with the price staying at about the same level, and average media hype impact volatility is insignificant. The immediate return on the next news is estimated to be very small, whereas the daily expected return is at this time at -0.07%. %. The volatility of related hype on Clean Energy is about 0.0%, with the expected price after the next announcement by competition of 2.46. About 21.0% of the company outstanding shares are owned by insiders. The company has Price to Book (P/B) ratio of 1.8. Historically many companies with similar price-to-book (P/B) ratio do better than the market in the long run. Clean Energy Fuels recorded a loss per share of 0.21. The entity had not issued any dividends in recent years. Assuming the 90 days horizon the next estimated press release will be any time. Check out Clean Energy Basic Forecasting Models 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 | |||
| RLI | Reliance Industries Limited | 0.00 | 0 per month | 0.00 | (0.15) | 1.92 | (2.44) | 6.60 | |
| MPN | Marathon Petroleum Corp | 0.00 | 0 per month | 0.00 | (0.12) | 3.83 | (3.77) | 13.10 | |
| V1L | Valero Energy | 0.00 | 0 per month | 0.00 | (0.04) | 3.72 | (3.28) | 10.28 | |
| NEFA | NESTE OYJ UNSPADR | 0.00 | 0 per month | 0.00 | (0.19) | 3.07 | (5.45) | 14.10 | |
| PKY1 | Polski Koncern Naftowy | 0.00 | 0 per month | 0.00 | (0.13) | 4.06 | (3.39) | 10.77 | |
| 7F8 | PTT OILRETBUS FOR BA10 | 0.00 | 0 per month | 0.00 | (0.25) | 2.78 | (2.94) | 9.71 |
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 |
About Clean Energy Predictive Indicators
The successful prediction of Clean Energy stock price could yield a significant profit to investors. But is it possible? The efficient-market hypothesis suggests that all published stock prices of traded companies, such as Clean Energy Fuels, already reflect all publicly available information. This academic statement is a fundamental principle of many financial and investing theories used today. However, the typical investor usually disagrees with a 'textbook' version of this hypothesis and continually tries to find mispriced stocks to increase returns. We use internally-developed statistical techniques to arrive at the intrinsic value of Clean Energy based on analysis of Clean Energy hews, social hype, general headline patterns, and widely used predictive technical indicators.
We also calculate exposure to Clean Energy's market risk, different technical and fundamental indicators, relevant financial multiples and ratios, and then comparing them to Clean Energy's related companies.
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 | 222.4 M | |
| Short Long Term Debt | 93 K |
Complementary Tools for Clean Stock analysis
When running Clean Energy's price analysis, check to measure Clean Energy's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Clean Energy is operating at the current time. Most of Clean Energy's value examination focuses on studying past and present price action to predict the probability of Clean Energy's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Clean Energy's price. Additionally, you may evaluate how the addition of Clean Energy to your portfolios can decrease your overall portfolio volatility.
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