Data Modul Stock Forward View - Polynomial Regression
| DAM Stock | EUR 26.80 0.20 0.75% |
This page provides Polynomial Regression reference data for Data Modul AG, calculated from historical daily prices. The model output shown here is derived from Data Modul's historical price series and is provided for informational purposes.
The Polynomial Regression forecasted value of Data Modul AG on the next trading day is expected to be 25.84 with a mean absolute deviation of 0.27 and the sum of the absolute errors of 16.28.A single variable polynomial regression model attempts to put a curve through the Data Modul historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm The Polynomial Regression reference information for Data Modul is based on available price data and is intended for informational purposes. Polynomial Regression Price Forecast For the 26th of March
Given 90 days horizon, the Polynomial Regression forecasted value of Data Modul AG on the next trading day is expected to be 25.84 with a mean absolute deviation of 0.27 , mean absolute percentage error of 0.17 , and the sum of the absolute errors of 16.28 .Please note that although there have been many attempts to predict Data 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 Data Modul's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Stock Forecast Pattern
| Backtest Data Modul | Data Modul Price Prediction | Research Analysis |
Forecasted Value
This next-day forecast for Data Modul AG uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. No forecasting approach has been shown to beat all others over time. Investors should treat any model output as a guide, not a guarantee.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of Data Modul stock data series using in forecasting. Note that when a statistical model is used to represent Data Modul 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 | 116.3645 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.2669 |
| MAPE | Mean absolute percentage error | 0.0098 |
| SAE | Sum of the absolute errors | 16.2832 |
Other Forecasting Options for Data Modul
The autocorrelation structure of Data Modul's daily returns reveals whether Data exhibits momentum, mean-reversion, or random-walk behavior. Separating these elements helps distinguish persistent directional moves from temporary noise in Data Stock price data.Data Modul Related Equities
These related stocks within the Materials space give benchmarks for judging Data Modul's results, margins, and growth trend. Revenue and margin checks across this group help investors set expectations for Data Modul's results.
| Risk & Return | Correlation |
Data Modul Market Strength Events
Market strength indicators applied to Data Modul stock help assess momentum and resilience across environments. These indicators support informed market timing decisions when analyzing Data Modul.
| Rate Of Daily Change | 1.01 | |||
| Day Median Price | 26.8 | |||
| Day Typical Price | 26.8 | |||
| Price Action Indicator | 0.1 | |||
| Period Momentum Indicator | 0.2 |
Data Modul Risk Indicators
Risk indicator analysis for Data Modul is essential for accurately projecting its future price trajectory. The process involves identifying the amount of risk involved in Data Modul's investment and either accepting or mitigating it.
| Mean Deviation | 1.07 | |||
| Standard Deviation | 2.15 | |||
| Variance | 4.61 |
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 Data Modul
Story coverage around Data Modul AG often expands when market conditions, narrative momentum, or risk-adjusted performance make the security more visible to investors. The practical risk is that faster visibility can increase both interest and skepticism at the same time.
Other Macroaxis Stories
Macroaxis story coverage is designed for a broad investing audience that ranges from self-directed traders to advisers, researchers, and institutional market participants. The content is intended to support people who want a more structured path from headline information to portfolio action.
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Data Modul Short Properties
A short-interest review of Data Modul AG provides context for understanding whether skepticism in the market is becoming more influential. This is most valuable when investors want to know whether bearish pressure is starting to shape the market's reaction function.
| Common Stock Shares Outstanding | 3.5 M | |
| Dividends Paid | -423 K |
More Resources for Data Stock Analysis
Other Information on Investing in Data Stock
The ratio set for Data Modul connects key financial figures across reports. The structure supports consistent evaluation across periods.