Data Modul (Germany) Price Prediction
| DAM Stock | EUR 26.80 0.20 0.74% |
Oversold Vs Overbought
53
Oversold | Overbought |
Using Data Modul hype-based prediction, you can estimate the value of Data Modul AG from the perspective of Data Modul 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 Data Modul to buy its stock at a price that has no basis in reality. In that case, they are not buying Data 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.
Data Modul after-hype prediction price | EUR 26.8 |
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.
Data |
Data Modul After-Hype Price Prediction Density Analysis
As far as predicting the price of Data Modul 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 Data Modul 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 Data Modul, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
Data Modul Estimiated After-Hype Price Volatility
In the context of predicting Data Modul's stock value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on Data Modul's historical news coverage. Data Modul's after-hype downside and upside margins for the prediction period are 23.75 and 29.85, respectively. We have considered Data Modul'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
Data Modul is not too volatile at this time. Analysis and calculation of next after-hype price of Data Modul AG is based on 3 months time horizon.
Data Modul Stock Price Prediction Analysis
Have you ever been surprised when a price of a Company such as Data Modul is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading Data Modul 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 Data Modul, 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 |
0.14 | 3.05 | 0.00 | 0.00 | 0 Events / Month | 0 Events / Month | In a few days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | ||
26.80 | 26.80 | 0.00 |
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Data Modul Hype Timeline
Data Modul AG is currently traded for 26.80on Hamburg Exchange of Germany. The entity stock is not elastic to its hype. The average elasticity to hype of competition is 0.0. Data is expected 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 expected to be very small, whereas the daily expected return is currently at 0.14%. %. The volatility of related hype on Data Modul is about 0.0%, with the expected price after the next announcement by competition of 26.80. The company has price-to-book (P/B) ratio of 1.59. Some equities with similar Price to Book (P/B) outperform the market in the long run. Data Modul AG last dividend was issued on the May 10, 2019. Assuming the 90 days trading horizon the next expected press release will be in a few days. Check out Data Modul Basic Forecasting Models to cross-verify your projections.Data Modul Related Hype Analysis
Having access to credible news sources related to Data Modul's direct competition is more important than ever and may enhance your ability to predict Data Modul's future price movements. Getting to know how Data Modul'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 Data Modul may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| APC | Apple Inc | 0.00 | 0 per month | 1.18 | 0.17 | 2.22 | (2.12) | 6.45 | |
| APC | Apple Inc | 0.00 | 0 per month | 1.02 | 0.18 | 1.81 | (1.96) | 5.21 | |
| APC | Apple Inc | 0.00 | 0 per month | 1.12 | 0.18 | 1.88 | (1.78) | 6.46 | |
| APC | Apple Inc | 0.00 | 0 per month | 0.89 | 0.16 | 2.83 | (2.01) | 5.24 | |
| MSF | Microsoft | 0.00 | 0 per month | 1.51 | 0.06 | 2.42 | (1.93) | 11.42 | |
| MSF | Microsoft | 0.00 | 0 per month | 1.39 | 0.06 | 2.35 | (1.92) | 9.11 | |
| MSF | Microsoft | 0.00 | 0 per month | 1.34 | 0.06 | 2.04 | (1.84) | 8.79 | |
| MSF | Microsoft | 0.00 | 0 per month | 1.29 | 0.07 | 2.55 | (1.61) | 8.71 |
Data Modul Additional Predictive Modules
Most predictive techniques to examine Data price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for Data using various technical indicators. When you analyze Data 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 Data Modul Predictive Indicators
The successful prediction of Data Modul 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 Data Modul AG, 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 Data Modul based on analysis of Data Modul hews, social hype, general headline patterns, and widely used predictive technical indicators.
We also calculate exposure to Data Modul's market risk, different technical and fundamental indicators, relevant financial multiples and ratios, and then comparing them to Data Modul's related companies.
Story Coverage note for Data Modul
The number of cover stories for Data Modul depends on current market conditions and Data Modul's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that Data Modul 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 Data Modul's long-term prospects. So, having above-average coverage will typically attract above-average short interest, leading to significant price volatility.
Other Macroaxis Stories
Our audience includes start-ups and big corporations as well as marketing, public relation firms, and advertising agencies, including technology and finance journalists. Our platform and its news and story outlet are popular among finance students, amateur traders, self-guided investors, entrepreneurs, retirees and baby boomers, academic researchers, financial advisers, as well as professional money managers - a very diverse and influential demographic landscape united by one goal - build optimal investment portfolios
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Data Modul Short Properties
Data Modul's future price predictability will typically decrease when Data Modul's long traders begin to feel the short-sellers pressure to drive the price lower. The predictive aspect of Data Modul AG often depends not only on the future outlook of the potential Data Modul'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. Data Modul's indicators that are reflective of the short sentiment are summarized in the table below.
| Common Stock Shares Outstanding | 3.5 M | |
| Dividends Paid | -423 K | |
| Short Long Term Debt | 15 M |
Other Information on Investing in Data Stock
Data Modul financial ratios help investors to determine whether Data Stock is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Data with respect to the benefits of owning Data Modul security.