Investors can use this prediction interface to forecast Best Buy historic prices and determine the direction of Best Buy Co 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 Best Buy 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 Best Buy Co systematic risks associated with finding meaningful patterns of Best Buy fundamentals over time. Check also Historical Fundamental Analysis of Best Buy to cross-verify your projections.
Simple Regression model is a single variable regression model that attempts to put a straight line through Best Buy 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.
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 Best Buy Co historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.