Watches Of Pink Sheet Forward View - Simple Regression

WOSGF Stock  USD 5.92  -0.10  -1.66%   
This page provides Simple Regression reference data for Watches of Switzerland, calculated from historical daily prices. The forecast output and associated deviation metrics are shown for informational use.
The Simple Regression forecasted value of Watches of Switzerland on the next trading day is expected to be 6.52 with a mean absolute deviation of 0.28 and the sum of the absolute errors of 17.27.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 Watches of Switzerland historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data. Watches Of's Simple Regression reference data is provided for informational and analytical purposes and does not constitute a trading recommendation.
Simple Regression model is a single variable regression model that attempts to put a straight line through Watches Of 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.

Simple Regression Price Forecast For the 23rd of March

Given 90 days horizon, the Simple Regression forecasted value of Watches of Switzerland on the next trading day is expected to be 6.52 with a mean absolute deviation of 0.28 , mean absolute percentage error of 0.12 , and the sum of the absolute errors of 17.27 .
Please note that although there have been many attempts to predict Watches Pink Sheet prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Watches Of's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Pink Sheet Forecast Pattern

Backtest Watches Of  Watches Of Price Prediction  Research Analysis  

Forecasted Value

For the next trading day, Macroaxis evaluates Watches Of's predictive range by looking for statistically meaningful downside and upside boundaries. The current forecast range spans downside near 3.75 and upside near 9.30.
Market Value
5.92
6.52
Expected Value
9.30
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 Watches Of pink sheet data series using in forecasting. Note that when a statistical model is used to represent Watches Of 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 Criteria116.0133
BiasArithmetic mean of the errors None
MADMean absolute deviation0.2831
MAPEMean absolute percentage error0.0425
SAESum of the absolute errors17.2687
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 Watches of Switzerland historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Other Forecasting Options for Watches Of

The price movement of Watches is a central concern for all potential investors, regardless of their level of expertise. Watches Pink Sheet price charts can be difficult to interpret due to the noise present in the data.

Watches Of Related Equities

The following equities are related to Watches Of within the Luxury Goods space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Watches Of against peers on metrics such as P/E, margins, and return on equity helps contextualize its positioning and identify relative strengths or weaknesses.
 Risk & Return  Correlation

Watches Of Market Strength Events

Market strength indicators applied to Watches Of pink sheet help investors assess the relative momentum and resilience of the security in different market environments. By using these indicators, traders can make more informed decisions about when to buy or sell Watches of Switzerland.

Watches Of Risk Indicators

Risk indicator analysis for Watches Of is essential for accurately projecting its future price trajectory. By identifying the level of risk embedded in Watches Of's investment, investors can make informed decisions about position sizing and risk mitigation.
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.

Story Coverage note for Watches Of

The amount of media and story coverage tied to Watches of Switzerland can signal where market attention is concentrating at the moment. This is most useful when investors want to understand why a security is suddenly drawing more public discussion.

Other Macroaxis Stories

Macroaxis publishes story content for a diverse readership that includes finance students, independent investors, money managers, and market-focused operating teams. What connects that audience is a focus on building stronger portfolios through better research, risk awareness, and comparative analysis.

More Resources for Watches Pink Sheet Analysis

Other Information on Investing in Watches Pink Sheet

Financial ratios for Watches Of organize key financial data into structured relationships. They provide context across profit, cash flow, and overall value.