DATA MODUL (Germany) Market Value
| DAM Stock | EUR 27.20 0.60 2.26% |
| Symbol | DATA |
DATA MODUL 'What if' Analysis
In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to DATA MODUL's stock what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of DATA MODUL.
| 12/08/2024 |
| 01/07/2025 |
If you would invest 0.00 in DATA MODUL on December 8, 2024 and sell it all today you would earn a total of 0.00 from holding DATA MODUL or generate 0.0% return on investment in DATA MODUL over 30 days. DATA MODUL is related to or competes with Aedas Homes, ADDUS HOMECARE, MAG SILVER, 24SEVENOFFICE GROUP, Japan Tobacco, Globex Mining, and Yanzhou Coal. More
DATA MODUL Upside/Downside Indicators
Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure DATA MODUL's stock current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess DATA MODUL upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 2.45 | |||
| Information Ratio | 0.0403 | |||
| Maximum Drawdown | 12.97 | |||
| Value At Risk | (3.10) | |||
| Potential Upside | 5.43 |
DATA MODUL Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for DATA MODUL's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as DATA MODUL's standard deviation. In reality, there are many statistical measures that can use DATA MODUL historical prices to predict the future DATA MODUL's volatility.| Risk Adjusted Performance | 0.0465 | |||
| Jensen Alpha | 0.1242 | |||
| Total Risk Alpha | 0.0774 | |||
| Sortino Ratio | 0.0426 | |||
| Treynor Ratio | (0.20) |
DATA MODUL Backtested Returns
Currently, DATA MODUL is not too volatile. DATA MODUL secures Sharpe Ratio (or Efficiency) of 0.0666, which denotes the company had a 0.0666% return per unit of volatility over the last 3 months. We have found twenty-nine technical indicators for DATA MODUL , which you can use to evaluate the volatility of the entity. Please confirm DATA MODUL's Downside Deviation of 2.45, mean deviation of 1.92, and Market Risk Adjusted Performance of (0.19) to check if the risk estimate we provide is consistent with the expected return of 0.18%. DATA MODUL has a performance score of 5 on a scale of 0 to 100. The firm shows a Beta (market volatility) of -0.59, which means possible diversification benefits within a given portfolio. As returns on the market increase, returns on owning DATA MODUL are expected to decrease at a much lower rate. During the bear market, DATA MODUL is likely to outperform the market. DATA MODUL now shows a risk of 2.66%. Please confirm DATA MODUL total risk alpha, treynor ratio, value at risk, as well as the relationship between the sortino ratio and maximum drawdown , to decide if DATA MODUL will be following its price patterns.
Auto-correlation | -0.35 |
Poor reverse predictability
DATA MODUL has poor reverse predictability. Overlapping area represents the amount of predictability between DATA MODUL time series from 8th of December 2024 to 23rd of December 2024 and 23rd of December 2024 to 7th of January 2025. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of DATA MODUL price movement. The serial correlation of -0.35 indicates that nearly 35.0% of current DATA MODUL price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.35 | |
| Spearman Rank Test | 0.14 | |
| Residual Average | 0.0 | |
| Price Variance | 0.17 |
DATA MODUL lagged returns against current returns
Autocorrelation, which is DATA MODUL stock's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting DATA MODUL's stock expected returns. We can calculate the autocorrelation of DATA MODUL returns to help us make a trade decision. For example, suppose you find that DATA MODUL has exhibited high autocorrelation historically, and you observe that the stock is moving up for the past few days. In that case, you can expect the price movement to match the lagging time series.
Current and Lagged Values |
| Timeline |
DATA MODUL regressed lagged prices vs. current prices
Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If DATA MODUL stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if DATA MODUL stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in DATA MODUL stock over time.
Current vs Lagged Prices |
| Timeline |
DATA MODUL Lagged Returns
When evaluating DATA MODUL's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of DATA MODUL stock have on its future price. DATA MODUL autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, DATA MODUL autocorrelation shows the relationship between DATA MODUL stock current value and its past values and can show if there is a momentum factor associated with investing in DATA MODUL .
Regressed Prices |
| Timeline |
Thematic Opportunities
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Additional Tools for DATA Stock Analysis
When running DATA MODUL's price analysis, check to measure DATA MODUL'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 DATA MODUL is operating at the current time. Most of DATA MODUL's value examination focuses on studying past and present price action to predict the probability of DATA MODUL's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move DATA MODUL's price. Additionally, you may evaluate how the addition of DATA MODUL to your portfolios can decrease your overall portfolio volatility.