John Bean Stock Forecast - Polynomial Regression

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

John Bean Polynomial Regression Price Forecast For the 25th of January

Given 90 days horizon, the Polynomial Regression forecasted value of John Bean Technologies on the next trading day is expected to be 127.95 with a mean absolute deviation of 2.60, mean absolute percentage error of 11.50, and the sum of the absolute errors of 158.84.

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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 John Bean stock data series using in forecasting. Note that when a statistical model is used to represent John Bean 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 John Bean 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 John Bean

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as John Bean Technologies. 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 123.27 125.15 127.03 Details

Intrinsic Valuation Low Real High 99.44 101.32 137.85 Details

Bollinger Band Projection (param) Low Middle High 120.36 126.29 132.22 Details

Please note, it is not enough to conduct a financial or market analysis of a single entity such as John Bean. Your research has to be compared to or analyzed against John Bean's peers to derive any actionable benefits. When done correctly, John Bean's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in John Bean Technologies.

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