Facebook Polynomial Regression

FB -- USA Stock  

Quarterly Earning Report: October 30, 2019  

Investors can use this prediction interface to forecast Facebook historic prices and determine the direction of Facebook 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 Facebook 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 Facebook systematic risks associated with finding meaningful patterns of Facebook fundamentals over time. Additionally see Historical Fundamental Analysis of Facebook to cross-verify your projections.
Horizon     30 Days    Login   to change
Facebook polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Facebook as well as the accuracy indicators are determined from the period prices.
Given 30 days horizon, the value of Facebook on the next trading day is expected to be 179.031771

Facebook Prediction Pattern

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Facebook Forecasted Value

October 16, 2019
Market Value
Expected Value

Model Predictive Factors

AICAkaike Information Criteria120.9855
BiasArithmetic mean of the errors None
MADMean absolute deviation3.5018
MAPEMean absolute percentage error0.019
SAESum of the absolute errors213.6122
A single variable polynomial regression model attempts to put a curve through the Facebook 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

Facebook Risk Indicators

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