PT Data Stock Forecast - Simple Regression

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

PT Data Simple Regression Price Forecast For the 3rd of December

Given 90 days horizon, the Simple Regression forecasted value of PT Data Sinergitama on the next trading day is expected to be 126.59 with a mean absolute deviation of 7.44, mean absolute percentage error of 85.69, and the sum of the absolute errors of 453.82.

PT Data Stock Forecast Pattern

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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 PT Data stock data series using in forecasting. Note that when a statistical model is used to represent PT Data 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 PT Data Sinergitama 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 PT Data

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as PT Data Sinergitama. 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 109.08 112.00 114.92 Details

Intrinsic Valuation Low Real High 96.41 99.33 123.20 Details

Bollinger Band Projection (param) Low Middle High 106.67 123.46 140.25 Details

Other Forecasting Options for PT Data

For every potential investor in ELIT, whether a beginner or expert, PT Data's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. ELIT Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in ELIT. Basic forecasting techniques help filter out the noise by identifying PT Data's price trends.

PT Data Related Equities

Risk & Return

Correlation

PT Data Sinergitama Technical and Predictive Analytics

The stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of PT Data's price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of PT Data's current price.

PT Data Market Strength Events

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