Heidelberg Materials Stock Forecast - Simple Regression

Simple Regression model is a single variable regression model that attempts to put a straight line through Heidelberg Materials 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.

Heidelberg Materials Simple Regression Price Forecast For the 16th of December 2024

Given 90 days horizon, the Simple Regression forecasted value of Heidelberg Materials AG on the next trading day is expected to be 126.48 with a mean absolute deviation of 3.32, mean absolute percentage error of 16.45, and the sum of the absolute errors of 202.31.

Heidelberg Materials Stock Forecast Pattern

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Heidelberg Materials Price Prediction

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Heidelberg Materials Forecasted Value

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 Heidelberg Materials stock data series using in forecasting. Note that when a statistical model is used to represent Heidelberg Materials 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. 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 Heidelberg Materials AG historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Predictive Modules for Heidelberg Materials

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Heidelberg Materials. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.

Hype Prediction Low Estimated High 123.16 124.80 126.44 Details

Intrinsic Valuation Low Real High 112.32 139.36 141.00 Details

Bollinger Band Projection (param) Low Middle High 120.04 123.64 127.24 Details

Other Forecasting Options for Heidelberg Materials

For every potential investor in Heidelberg, whether a beginner or expert, Heidelberg Materials' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Heidelberg Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Heidelberg. Basic forecasting techniques help filter out the noise by identifying Heidelberg Materials' price trends.

Heidelberg Materials Related Equities

Risk & Return

Correlation

Heidelberg Materials Technical and Predictive Analytics

The stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Heidelberg Materials' price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of Heidelberg Materials' current price.

Heidelberg Materials Market Strength Events

Heidelberg Materials Risk Indicators

Additional Tools for Heidelberg Stock Analysis