URBAN OUTFITTERS Stock Forward View - Simple Moving Average
| UOF Stock | 55.12 -0.74 -1.32% |
Momentum
Sell Extended
Oversold | Overbought |
The summary pairs URBAN OUTFITTERS' headline activity with price response context.
The Simple Moving Average forecasted value of URBAN OUTFITTERS on the next trading day is expected to be 55.12 with a mean absolute deviation of 1.20 and the sum of the absolute errors of 71.87.URBAN OUTFITTERS after-hype prediction price | € 56.38 |
Sentiment indicators are framed alongside forecasting, technical analysis, analyst estimates, and momentum.
URBAN |
URBAN OUTFITTERS Additional Predictive Modules
Most predictive techniques to examine URBAN price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for URBAN using various technical indicators. When you analyze URBAN 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 |
Simple Moving Average Price Forecast For the 14th of March 2026
Given 90 days horizon, the Simple Moving Average forecasted value of URBAN OUTFITTERS on the next trading day is expected to be 55.12 with a mean absolute deviation of 1.20 , mean absolute percentage error of 2.99 , and the sum of the absolute errors of 71.87 .Please note that although there have been many attempts to predict URBAN 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 URBAN OUTFITTERS's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Stock Forecast Pattern
| Backtest URBAN OUTFITTERS | URBAN OUTFITTERS Price Prediction | Research Analysis |
Forecasted Value
This next-day forecast for URBAN OUTFITTERS uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. Investors should still remember that no empirical framework consistently proves that one family of forecasting models will outperform all other approaches in live markets.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Simple Moving Average forecasting method's relative quality and the estimations of the prediction error of URBAN OUTFITTERS stock data series using in forecasting. Note that when a statistical model is used to represent URBAN OUTFITTERS 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 | 117.3669 |
| Bias | Arithmetic mean of the errors | 0.3558 |
| MAD | Mean absolute deviation | 1.1978 |
| MAPE | Mean absolute percentage error | 0.0197 |
| SAE | Sum of the absolute errors | 71.87 |
Mean reversion in URBAN OUTFITTERS is distinct from trend following. Where trend followers ride price momentum, mean reversion investors bet that extended moves will reverse once the underlying driver runs out of fuel.
After-Hype Price Density Analysis
Probability distribution analysis for URBAN OUTFITTERS provides an objective framework for evaluating risk/reward tradeoffs. By comparing the width of URBAN OUTFITTERS's upside distribution against the downside, investors can make more calibrated position sizing decisions.
Next price density |
| Expected price to next headline |
Estimiated After-Hype Price Volatility
The empirical analysis of URBAN OUTFITTERS's news impact provides an evidence-based estimate of potential price movement around upcoming announcements. URBAN OUTFITTERS's after-hype downside and upside margins for the prediction period are 54.08 and 58.68, respectively. This estimate is conditional on the type and significance of the news event and should be interpreted in that context for URBAN OUTFITTERS.
Current Value
The after-hype framework applied to URBAN OUTFITTERS assumes a 3 months review window and focuses on post-sentiment normalization rather than raw momentum. This view is most useful when investors want to compare sentiment-driven price extension with a more measured post-news scenario.
Price Outlook Analysis
Have you ever been surprised when a price of a Company such as URBAN OUTFITTERS is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading URBAN OUTFITTERS 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 URBAN OUTFITTERS, 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 |
0.36 | 2.31 | 0.57 | 0.01 | 8 Events | 1 Events | In 8 days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
55.12 | 56.38 | 0.93 |
|
Hype Timeline
URBAN OUTFITTERS is at this time traded for 55.12on Stuttgart Exchange of Germany. The company has historical hype elasticity of 0.57, and average elasticity to hype of competition of 0.01. URBAN is anticipated to increase in value after the next headline, with the price projected to jump to 56.38 or above. The average volatility of media hype impact on the company the price is about 146.2%. The price appreciation on the next news is anticipated to be 0.93%, whereas the daily expected return is at this time at -0.36%. The volatility of related hype on URBAN OUTFITTERS is about 5977.82%, with the expected price after the next announcement by competition of 55.13. The company reported revenue of 5.55 B. Net Income was 402.46 M with profit before overhead, payroll, taxes, and interest of 0. Assuming the 90-day trading horizon the next anticipated press release will be in 8 days. Historical Fundamental Analysis of URBAN OUTFITTERS provides a cross-check on projections for URBAN OUTFITTERS. The historical view provides additional context.Related Hype Analysis
By analyzing how URBAN OUTFITTERS's sector peers have historically reacted to different types of news, investors can build a mental model of the sentiment dynamics that typically precede changes in URBAN OUTFITTERS's own price.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| 14D | tokentus investment AG | -0.02 | 2 per month | 4.34 | 0.01 | 5.88 | -6.25 | 56.92 | |
| 1KT | Keysight Technologies | 0.56 | 6 per month | 1.53 | 0.17 | 3.92 | -2.96 | 28.17 | |
| JAN | Japan Asia Investment | 0.01 | 2 per month | 0.00 | -0.07 | 3.19 | -3.74 | 10.41 | |
| AMK | Amkor Technology | -1.76 | 9 per month | 4.40 | 0.06 | 7.71 | -7.16 | 25.02 | |
| GUG | Guangdong Investment Limited | -0.01 | 6 per month | 0.00 | -0.0006 | 2.86 | -3.80 | 8.12 | |
| TE1 | Bio Techne Corp | 1.49 | 7 per month | 0.00 | -0.09 | 3.74 | -4.15 | 13.09 | |
| CUG | Chuangs China Investments | 0.0005 | 2 per month | 3.48 | 0.03 | 8.70 | -7.69 | 21.33 |
Other Forecasting Options for URBAN OUTFITTERS
Investors evaluating URBAN at any level need to understand the significance of URBAN OUTFITTERS's price movement for their investment outcomes. The presence of noise in URBAN Stock price charts demands careful analysis to avoid misinterpreting short-term fluctuations as trends.URBAN OUTFITTERS Related Equities
The following equities are related to URBAN OUTFITTERS within the Materials space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing URBAN OUTFITTERS against peers on metrics such as P/E, margins, and return on equity helps contextualize its positioning and identify relative strengths or weaknesses.
| Risk & Return | Correlation |
URBAN OUTFITTERS Market Strength Events
Market strength indicators applied to URBAN OUTFITTERS help investors evaluate how the stock tracks overall market momentum and conditions. These signals are used to determine optimal timing for entering or exiting URBAN OUTFITTERS positions.
URBAN OUTFITTERS Risk Indicators
The assessment of URBAN OUTFITTERS's risk indicators plays a key role in forecasting its future price and managing investment exposure. Investors who measure URBAN OUTFITTERS's risk profile carefully are better equipped to decide how to manage their positions.
| Mean Deviation | 1.83 | |||
| Standard Deviation | 2.42 | |||
| Variance | 5.83 |
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 URBAN OUTFITTERS
Coverage intensity for URBAN OUTFITTERS matters because narrative visibility can influence sentiment, participation, and volatility around the name. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.
Other Macroaxis Stories
Story coverage on Macroaxis is built for readers who approach markets from different levels of experience but share the same need for disciplined investment context. Used well, these stories become part of a broader workflow built around idea generation, validation, and risk-adjusted portfolio design.
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