Carbon Streaming OTC Stock Forecast - Polynomial Regression

OFSTF Stock  USD 0.31  0.01  3.13%   
The Polynomial Regression forecasted value of Carbon Streaming Corp on the next trading day is expected to be 0.31 with a mean absolute deviation of 0.01 and the sum of the absolute errors of 0.74. Carbon OTC Stock Forecast is based on your current time horizon. We recommend always using this module together with an analysis of Carbon Streaming's historical fundamentals, such as revenue growth or operating cash flow patterns.
  
Carbon Streaming polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Carbon Streaming Corp as well as the accuracy indicators are determined from the period prices.

Carbon Streaming Polynomial Regression Price Forecast For the 25th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Carbon Streaming Corp on the next trading day is expected to be 0.31 with a mean absolute deviation of 0.01, mean absolute percentage error of 0.0002, and the sum of the absolute errors of 0.74.
Please note that although there have been many attempts to predict Carbon OTC Stock prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Carbon Streaming's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Carbon Streaming OTC Stock Forecast Pattern

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Carbon Streaming Forecasted Value

In the context of forecasting Carbon Streaming's OTC Stock value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. Carbon Streaming's downside and upside margins for the forecasting period are 0 and 6.01, respectively. We have considered Carbon Streaming's daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Market Value
0.31
0.31
Expected Value
6.01
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of Carbon Streaming otc stock data series using in forecasting. Note that when a statistical model is used to represent Carbon Streaming otc 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.
AICAkaike Information Criteria109.6286
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0121
MAPEMean absolute percentage error0.0316
SAESum of the absolute errors0.7365
A single variable polynomial regression model attempts to put a curve through the Carbon Streaming historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm

Predictive Modules for Carbon Streaming

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Carbon Streaming Corp. Regardless of method or technology, however, to accurately forecast the otc stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the otc 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.
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Carbon Streaming'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
0.020.316.01
Details
Intrinsic
Valuation
LowRealHigh
0.010.295.99
Details

Other Forecasting Options for Carbon Streaming

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

Carbon Streaming Related Equities

One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with Carbon Streaming otc stock to make a market-neutral strategy. Peer analysis of Carbon Streaming could also be used in its relative valuation, which is a method of valuing Carbon Streaming by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

Carbon Streaming Corp Technical and Predictive Analytics

The otc stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Carbon Streaming'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 Carbon Streaming's current price.

Carbon Streaming Market Strength Events

Market strength indicators help investors to evaluate how Carbon Streaming otc stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Carbon Streaming shares will generate the highest return on investment. By undertsting and applying Carbon Streaming otc stock market strength indicators, traders can identify Carbon Streaming Corp entry and exit signals to maximize returns.

Carbon Streaming Risk Indicators

The analysis of Carbon Streaming's basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Carbon Streaming's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting carbon otc stock prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.

Currently Active Assets on Macroaxis

Other Information on Investing in Carbon OTC Stock

Carbon Streaming financial ratios help investors to determine whether Carbon OTC 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 Carbon with respect to the benefits of owning Carbon Streaming security.