Ford Motor Polynomial Regression

F -- USA Stock  

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Investors can use this prediction interface to forecast Ford Motor historic prices and determine the direction of Ford Motor Company future trends based on various well-known forecasting models. However looking at historical price movement exclusively is usually misleading. Macroaxis recommends to always use this module together with analysis of Ford Motor historical fundamentals such as revenue growth or operating cash flow patterns. Although naive historical forecasting may sometimes provide an important future outlook for the firm we recommend to always cross-verify it against solid analysis of Ford Motor Company systematic risks associated with finding meaningful patterns of Ford Motor fundamentals over time. Additionally see Historical Fundamental Analysis of Ford Motor to cross-verify your projections.
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Horizon     30 Days    Login   to change
Ford Motor polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Ford Motor Company as well as the accuracy indicators are determined from the period prices.
Given 30 days horizon, the value of Ford Motor Company on the next trading day is expected to be 8.936413

Ford Motor Prediction Pattern

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Ford Motor Forecasted Value

Market Value
9.02
December 6, 2019
8.94
Expected Value
13.65
Upside

Model Predictive Factors

AICAkaike Information Criteria114.613
BiasArithmetic mean of the errors None
MADMean absolute deviation0.1365
MAPEMean absolute percentage error0.0153
SAESum of the absolute errors8.3235
A single variable polynomial regression model attempts to put a curve through the Ford Motor 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 + ... + am*Xm

Volatility Measures

Ford Motor Risk Indicators

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