|F -- USA Stock|| |
USD 11.85 0.14 1.17%
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.
Simple Regression model is a single variable regression model that attempts to put a straight line through Ford Motor 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.
Given 30 days horizon, the value of Ford Motor Company on the next trading day is expected to be 12.124926
Ford Motor Prediction Pattern
Ford Motor Forecasted Value
June 19, 2018
Next Trading Day Expected Value
Model Predictive Factors
|AIC||Akaike Information Criteria||33.3017|
|Bias||Arithmetic mean of the errors ||None|
|MAD||Mean absolute deviation||0.1203|
|MAPE||Mean absolute percentage error||0.0101|
|SAE||Sum of the absolute errors||2.0447|
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 Ford Motor Company historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.