Generation Mining Stock Statistic Functions Linear Regression Intercept

GENM Stock  CAD 0.27  0.01  3.85%   
Generation Mining statistic functions tool provides the execution environment for running the Linear Regression Intercept function and other technical functions against Generation Mining. Generation Mining value trend is the prevailing direction of the price over some defined period of time. The concept of trend is an important idea in technical analysis, including the analysis of statistic functions indicators. As with most other technical indicators, the Linear Regression Intercept function function is designed to identify and follow existing trends. Generation Mining statistical functions help analysts to determine different price movement patterns based on how price series statistical indicators change over time. Please specify Time Period to run this model.

Function
Time Period
Execute Function
The output start index for this execution was twenty-three with a total number of output elements of thirty-eight. The Linear Regression Intercept is the expected mean value of Generation Mining price seriese where values of its benchmark or peer price series are zero.

Generation Mining Technical Analysis Modules

Most technical analysis of Generation Mining help investors determine whether a current trend will continue and, if not, when it will shift. We provide a combination of tools to recognize potential entry and exit points for Generation from various momentum indicators to cycle indicators. When you analyze Generation charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.

About Generation Mining Predictive Technical Analysis

Predictive technical analysis modules help investors to analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Generation Mining. We use our internally-developed statistical techniques to arrive at the intrinsic value of Generation Mining based on widely used predictive technical indicators. In general, we focus on analyzing Generation Stock price patterns and their correlations with different microeconomic environment and drivers. We also apply predictive analytics to build Generation Mining's daily price indicators and compare them against related drivers, such as statistic functions and various other types of predictive indicators. Using this methodology combined with a more conventional technical analysis and fundamental analysis, we attempt to find the most accurate representation of Generation Mining's intrinsic value. In addition to deriving basic predictive indicators for Generation Mining, we also check how macroeconomic factors affect Generation Mining price patterns. Please read more on our technical analysis page or use our predictive modules below to complement your research.
 2023 2024 (projected)
Payables Turnover786.13825.44
Days Of Inventory On Hand74.8766.55
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Generation Mining's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
Hype
Prediction
LowEstimatedHigh
0.010.264.95
Details
Intrinsic
Valuation
LowRealHigh
0.010.224.91
Details
Naive
Forecast
LowNextHigh
0.010.274.96
Details
Bollinger
Band Projection (param)
LowerMiddle BandUpper
0.260.270.29
Details

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Other Information on Investing in Generation Stock

Generation Mining financial ratios help investors to determine whether Generation Stock is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Generation with respect to the benefits of owning Generation Mining security.