Ford Motor Polynomial Regression

Ford Motor Company -- USA Stock  

USD 12.58  0.03  0.24%

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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Investment 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 12.481912

Ford Motor Prediction Pattern

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Ford Motor Forecasted Value

December 12, 2017
12.58
Market Value
Downside upside
12.48
Next Trading Day Expected Value
Target Price Odds
 Above  Below  
15.18
Upside
Upside upside

Model Predictive Factors

AICAkaike Information Criteria32.7033
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0876
MAPEMean absolute percentage error0.0071
SAESum of the absolute errors1.4894
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