Ford Motor Simple Exponential Smoothing

Ford Motor Company -- USA Stock  

USD 12.14  0.04  0.33%

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 simple exponential smoothing forecast is a very popular model used to produce a smoothed price series. Whereas in simple Moving Average models the past observations for Ford Motor Company are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as Ford Motor prices get older.
Given 30 days horizon, the value of Ford Motor Company on the next trading day is expected to be 12.14

Ford Motor Prediction Pattern

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Ford Motor Forecasted Value

October 23, 2017
12.14
Market Value
Downside upside
12.14
Next Trading Day Expected Value
Target Price Odds
 Above  Below  
14.67
Upside
Upside upside

Model Predictive Factors

AICAkaike Information Criteria31.094
BiasArithmetic mean of the errors -0.0106
MADMean absolute deviation0.0906
MAPEMean absolute percentage error0.0074
SAESum of the absolute errors1.45
This simple exponential smoothing model begins by setting Ford Motor Company forecast for the second period equal to the observation of the first period. In other words, recent Ford Motor observations are given relatively more weight in forecasting than the older observations.