Brightcove Stock Forecast - Simple Regression

Simple Regression model is a single variable regression model that attempts to put a straight line through Brightcove 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.

Brightcove Simple Regression Price Forecast For the 21st of March

Given 90 days horizon, the Simple Regression forecasted value of Brightcove on the next trading day is expected to be 4.88 with a mean absolute deviation of 0.34, mean absolute percentage error of 0.15, and the sum of the absolute errors of 20.49.

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Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of Brightcove stock data series using in forecasting. Note that when a statistical model is used to represent Brightcove stock, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality. 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 Brightcove historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Predictive Modules for Brightcove

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Brightcove. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.

Hype Prediction Low Estimated High 4.19 4.45 4.71 Details

Intrinsic Valuation Low Real High 3.12 3.38 4.90 Details

Bollinger Band Projection (param) Low Middle High 4.38 4.42 4.46 Details

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