Skylight Health Pink Sheet Forecast - Polynomial Regression
The Polynomial Regression forecasted value of Skylight Health Group on the next trading day is expected to be 0.000074 with a mean absolute deviation of 0.00001 and the sum of the absolute errors of 0.0006. Skylight Pink Sheet Forecast is based on your current time horizon. We recommend always using this module together with an analysis of Skylight Health's historical fundamentals, such as revenue growth or operating cash flow patterns.
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Skylight Health Polynomial Regression Price Forecast For the 2nd of January
Given 90 days horizon, the Polynomial Regression forecasted value of Skylight Health Group on the next trading day is expected to be 0.000074 with a mean absolute deviation of 0.00001, mean absolute percentage error of 0, and the sum of the absolute errors of 0.0006.Please note that although there have been many attempts to predict Skylight Pink Sheet 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 Skylight Health's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Skylight Health Pink Sheet Forecast Pattern
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Skylight Health Forecasted Value
In the context of forecasting Skylight Health's Pink Sheet 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. Skylight Health's downside and upside margins for the forecasting period are 0.00 and 127.84, respectively. We have considered Skylight Health'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.
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 Skylight Health pink sheet data series using in forecasting. Note that when a statistical model is used to represent Skylight Health pink sheet, 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.| AIC | Akaike Information Criteria | 96.2578 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0 |
| MAPE | Mean absolute percentage error | 9.223372036854776E14 |
| SAE | Sum of the absolute errors | 6.0E-4 |
Predictive Modules for Skylight Health
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Skylight Health Group. Regardless of method or technology, however, to accurately forecast the pink sheet market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the pink sheet 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.Other Forecasting Options for Skylight Health
For every potential investor in Skylight, whether a beginner or expert, Skylight Health's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Skylight Pink Sheet price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Skylight. Basic forecasting techniques help filter out the noise by identifying Skylight Health's price trends.Skylight Health 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 Skylight Health pink sheet to make a market-neutral strategy. Peer analysis of Skylight Health could also be used in its relative valuation, which is a method of valuing Skylight Health by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Skylight Health Group Technical and Predictive Analytics
The pink sheet market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Skylight Health'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 Skylight Health's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
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| DD | Dupont De Nemours | |
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| GOOG | Alphabet Inc Class C | |
| BAC | Bank of America | |
| CRM | Salesforce |
Other Information on Investing in Skylight Pink Sheet
Skylight Health financial ratios help investors to determine whether Skylight Pink Sheet 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 Skylight with respect to the benefits of owning Skylight Health security.