Macys Polynomial Regression

M -- USA Stock  

USD 25.79  1.04  0.04%

Investors can use this prediction interface to forecast Macys historic prices and determine the direction of Macys 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 Macys 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 Macys systematic risks associated with finding meaningful patterns of Macys fundamentals over time. Please see also Historical Fundamental Analysis of Macys to cross-verify your projections.
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Horizon     30 Days    Login   to change
Macys polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Macys as well as the accuracy indicators are determined from the period prices.
Given 30 days horizon, the value of Macys on the next trading day is expected to be 23.900505

Macys Prediction Pattern

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Macys Forecasted Value

January 20, 2019
25.79
Market Value
23.90
Next Trading Day Expected Value
Target Odds
Odds Odds
35.20
Upside

Model Predictive Factors

AICAkaike Information Criteria78.2878
BiasArithmetic mean of the errors None
MADMean absolute deviation1.0589
MAPEMean absolute percentage error0.0359
SAESum of the absolute errors41.2969
A single variable polynomial regression model attempts to put a curve through the Macys 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
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