Extreme Networks Stock Forward View - Simple Regression

EXTR Stock  USD 14.96  -0.21  -1.38%   
The Simple Regression forecast reference data for Extreme Networks is based on the equity's recent trading history. Forecast values and accuracy indicators are summarized on this page for reference. This reference information is provided for analytical context.
The Simple Regression forecasted value of Extreme Networks on the next trading day is expected to be 13.87 with a mean absolute deviation of 0.50 and the sum of the absolute errors of 30.50.In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as Extreme Networks historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data. The Simple Regression projections for Extreme Networks are reference data based on historical daily prices and are provided as informational context.
Simple Regression model is a single variable regression model that attempts to put a straight line through Extreme Networks price points. This line is defined by its gradient or slope, and the point at which it intercepts the x-axis. Mathematically, assuming the independent variable is X and the dependent variable is Y, then this line can be represented as: Y = intercept + slope * X.

Simple Regression Price Forecast For the 25th of March

Given 90 days horizon, the Simple Regression forecasted value of Extreme Networks on the next trading day is expected to be 13.87 with a mean absolute deviation of 0.50 , mean absolute percentage error of 0.34 , and the sum of the absolute errors of 30.50 .
Please note that although there have been many attempts to predict Extreme 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 Extreme Networks' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Stock Forecast Pattern

Backtest Extreme Networks  Extreme Networks Price Prediction  Research Analysis  

Forecasted Value

This next-day forecast for Extreme Networks uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. The current forecast range spans downside near 11.89 and upside near 15.85.
Market Value
14.96
13.87
Expected Value
15.85
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of Extreme Networks stock data series using in forecasting. Note that when a statistical model is used to represent Extreme Networks 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.
AICAkaike Information Criteria117.0234
BiasArithmetic mean of the errors None
MADMean absolute deviation0.5
MAPEMean absolute percentage error0.0334
SAESum of the absolute errors30.5012
In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as Extreme Networks historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Other Forecasting Options for Extreme Networks

Volatility clustering is a well-documented feature of Extreme Stock price data where periods of large moves tend to follow other large moves. When Extreme Networks' RSI reaches extreme levels, it often precedes a short-term price correction or consolidation. Seasonal patterns in Extreme Networks' returns can persist when driven by structural factors like earnings calendars or index rebalancing.

Extreme Networks Related Equities

These stocks are related to Extreme Networks within the Information Technology space and can be used for peer review, pricing, or spreading risk. Key comparison metrics include price-to-earnings, profit margin, and revenue growth across Extreme Networks' peer group.
 Risk & Return  Correlation

Extreme Networks Market Strength Events

Analyzing market strength indicators for Extreme Networks enables investors to understand relative stock momentum. These tools help identify favorable windows for position changes in Extreme Networks. Market strength indicators support more precise timing of Extreme Networks positions across market cycles.

Extreme Networks Risk Indicators

Identifying and analyzing Extreme Networks' key risk indicators is a foundational step in projecting how its price may evolve. This process involves measuring the level of investment risk in Extreme Networks' and determining how best to manage it. Studying Extreme Networks' risk indicators helps investors understand the risk level of extreme stock.
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 Extreme Networks

Coverage intensity for Extreme Networks 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.

Extreme Networks Short Properties

A short-interest review of Extreme Networks 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 Outstanding132.3 M
Cash And Short Term Investments231.7 M

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