Citigroup Simple Regression

C -- USA Stock  

Earnings Call 

Investors can use this prediction interface to forecast Citigroup historic prices and determine the direction of Citigroup 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 Citigroup 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 Citigroup systematic risks associated with finding meaningful patterns of Citigroup fundamentals over time. Check also Historical Fundamental Analysis of Citigroup 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 Citigroup 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 Citigroup on the next trading day is expected to be 76.704548

Citigroup Prediction Pattern

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Citigroup Forecasted Value

Market Value
75.33
December 12, 2019
76.70
Expected Value
80.55
Upside

Model Predictive Factors

AICAkaike Information Criteria120.6481
BiasArithmetic mean of the errors None
MADMean absolute deviation1.1459
MAPEMean absolute percentage error0.016
SAESum of the absolute errors71.044
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 Citigroup 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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