Goosehead Insurance Stock Forecast - Polynomial Regression

Goosehead Insurance polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Goosehead Insurance as well as the accuracy indicators are determined from the period prices.

Goosehead Insurance Polynomial Regression Price Forecast For the 1st of December

Given 90 days horizon, the Polynomial Regression forecasted value of Goosehead Insurance on the next trading day is expected to be 124.90 with a mean absolute deviation of 2.09, mean absolute percentage error of 6.22, and the sum of the absolute errors of 127.77.

Goosehead Insurance Stock Forecast Pattern

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Goosehead Insurance Price Prediction

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Goosehead Insurance Forecasted Value

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 Goosehead Insurance stock data series using in forecasting. Note that when a statistical model is used to represent Goosehead Insurance 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. A single variable polynomial regression model attempts to put a curve through the Goosehead Insurance 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 + ... + a_m*X^m

Predictive Modules for Goosehead Insurance

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Goosehead Insurance. 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 117.07 119.60 122.13 Details

Intrinsic Valuation Low Real High 107.64 128.65 131.18 Details

Bollinger Band Projection (param) Low Middle High 108.86 117.68 126.50 Details

Other Forecasting Options for Goosehead Insurance

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

Goosehead Insurance Related Equities

Risk & Return

Correlation

Goosehead Insurance 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 Goosehead Insurance's 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 Goosehead Insurance's current price.

Goosehead Insurance Market Strength Events

Goosehead Insurance Risk Indicators

Additional Tools for Goosehead Stock Analysis