Facebook Simple Regression

FB -- USA Stock  

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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.
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Horizon     30 Days    Login   to change
Simple Regression model is a single variable regression model that attempts to put a straight line through Facebook 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 Facebook on the next trading day is expected to be 200.494361

Facebook Prediction Pattern

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Facebook Forecasted Value

Market Value
201.05
December 8, 2019
196.64
Downside
200.49
Expected Value
204.35
Upside

Model Predictive Factors

AICAkaike Information Criteria120.9613
BiasArithmetic mean of the errors None
MADMean absolute deviation3.2818
MAPEMean absolute percentage error0.0176
SAESum of the absolute errors200.192
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 Facebook historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

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