Kernel Group Stock Forecast - Polynomial Regression

The Polynomial Regression forecasted value of Kernel Group Holdings on the next trading day is expected to be 0.08 with a mean absolute deviation of 0.02 and the sum of the absolute errors of 1.01. Kernel Stock Forecast is based on your current time horizon.
  
Kernel Group polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Kernel Group Holdings as well as the accuracy indicators are determined from the period prices.

Kernel Group Polynomial Regression Price Forecast For the 29th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Kernel Group Holdings on the next trading day is expected to be 0.08 with a mean absolute deviation of 0.02, mean absolute percentage error of 0.0006, and the sum of the absolute errors of 1.01.
Please note that although there have been many attempts to predict Kernel 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 Kernel Group's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Kernel Group Stock Forecast Pattern

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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 Kernel Group stock data series using in forecasting. Note that when a statistical model is used to represent Kernel Group 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 Criteria110.6364
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0166
MAPEMean absolute percentage error9.223372036854776E14
SAESum of the absolute errors1.0136
A single variable polynomial regression model attempts to put a curve through the Kernel Group 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 Kernel Group

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Kernel Group Holdings. 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
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Intrinsic
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Kernel Group 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 Kernel Group stock to make a market-neutral strategy. Peer analysis of Kernel Group could also be used in its relative valuation, which is a method of valuing Kernel Group by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

Kernel Group Risk Indicators

The analysis of Kernel Group'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 Kernel Group's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting kernel 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.

Also Currently Popular

Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.
Check out Correlation Analysis to better understand how to build diversified portfolios. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in bureau of economic analysis.
You can also try the Financial Widgets module to easily integrated Macroaxis content with over 30 different plug-and-play financial widgets.

Other Consideration for investing in Kernel Stock

If you are still planning to invest in Kernel Group Holdings check if it may still be traded through OTC markets such as Pink Sheets or OTC Bulletin Board. You may also purchase it directly from the company, but this is not always possible and may require contacting the company directly. Please note that delisted stocks are often considered to be more risky investments, as they are no longer subject to the same regulatory and reporting requirements as listed stocks. Therefore, it is essential to carefully research the Kernel Group's history and understand the potential risks before investing.
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