Appswarm Pink Sheet Forecast - Simple Regression

SWRM Stock  USD 0.0003  0.0001  50.00%   
The Simple Regression forecasted value of Appswarm on the next trading day is expected to be 0.0003 with a mean absolute deviation of 0.000012 and the sum of the absolute errors of 0.0007. Appswarm Pink Sheet Forecast is based on your current time horizon.
  
Simple Regression model is a single variable regression model that attempts to put a straight line through Appswarm 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.

Appswarm Simple Regression Price Forecast For the 23rd of November

Given 90 days horizon, the Simple Regression forecasted value of Appswarm on the next trading day is expected to be 0.0003 with a mean absolute deviation of 0.000012, mean absolute percentage error of 0, and the sum of the absolute errors of 0.0007.
Please note that although there have been many attempts to predict Appswarm 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 Appswarm's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Appswarm Pink Sheet Forecast Pattern

Backtest AppswarmAppswarm Price PredictionBuy or Sell Advice 

Appswarm Forecasted Value

In the context of forecasting Appswarm'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. Appswarm's downside and upside margins for the forecasting period are 0.000003 and 15.11, respectively. We have considered Appswarm'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
0.0003
0.000003
Downside
0.0003
Expected Value
15.11
Upside

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 Appswarm pink sheet data series using in forecasting. Note that when a statistical model is used to represent Appswarm 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.
AICAkaike Information Criteria96.8709
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0
MAPEMean absolute percentage error0.0498
SAESum of the absolute errors7.0E-4
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 Appswarm 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 Appswarm

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Appswarm. 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.
Hype
Prediction
LowEstimatedHigh
0.000.000315.11
Details
Intrinsic
Valuation
LowRealHigh
0.000.000215.11
Details
Bollinger
Band Projection (param)
LowMiddleHigh
0.00030.00030.0003
Details

Other Forecasting Options for Appswarm

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

Appswarm 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 Appswarm pink sheet to make a market-neutral strategy. Peer analysis of Appswarm could also be used in its relative valuation, which is a method of valuing Appswarm by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

Appswarm 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 Appswarm'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 Appswarm's current price.

Appswarm Market Strength Events

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

Appswarm Risk Indicators

The analysis of Appswarm'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 Appswarm's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting appswarm pink sheet 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.

Building efficient market-beating portfolios requires time, education, and a lot of computing power!

The Portfolio Architect is an AI-driven system that provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for individual and institutional investors.

Try AI Portfolio Architect

Other Information on Investing in Appswarm Pink Sheet

Appswarm financial ratios help investors to determine whether Appswarm 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 Appswarm with respect to the benefits of owning Appswarm security.