Alpha Pro Stock Forward View - Double Exponential Smoothing
| APL Stock | EUR 3.90 -0.04 -1.02% |
Alpha Pro Tech's Double Exponential Smoothing reference page summarizes the forecasted price and model accuracy metrics derived from daily trading data. This reference information is provided for analytical context.
The Double Exponential Smoothing forecasted value of Alpha Pro Tech on the next trading day is expected to be 3.85 with a mean absolute deviation of 0.07 and the sum of the absolute errors of 4.29.When Alpha Pro Tech prices exhibit either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any Alpha Pro Tech trend in the prices. So in double exponential smoothing past observations are given exponentially smaller weights as the observations get older. In other words, recent Alpha Pro observations are given relatively more weight in forecasting than the older observations. This Double Exponential Smoothing forecast data for Alpha Pro Tech is sourced from the most recent available trading data and is intended solely as reference information. Double Exponential Smoothing Price Forecast For the 23rd of March
Given 90 days horizon, the Double Exponential Smoothing forecasted value of Alpha Pro Tech on the next trading day is expected to be 3.85 with a mean absolute deviation of 0.07 , mean absolute percentage error of 0.01 , and the sum of the absolute errors of 4.29 .Please note that although there have been many attempts to predict Alpha Stock 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 Alpha Pro's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Stock Forecast Pattern
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Forecasted Value
For the next trading day, Macroaxis evaluates Alpha Pro's predictive range by looking for statistically meaningful downside and upside boundaries. Investors should still remember that no empirical framework consistently proves that one family of forecasting models will outperform all other approaches in live markets.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Double Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of Alpha Pro stock data series using in forecasting. Note that when a statistical model is used to represent Alpha Pro 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.| AIC | Akaike Information Criteria | Huge |
| Bias | Arithmetic mean of the errors | 0.013 |
| MAD | Mean absolute deviation | 0.0727 |
| MAPE | Mean absolute percentage error | 0.0173 |
| SAE | Sum of the absolute errors | 4.2908 |
Other Forecasting Options for Alpha Pro
The movement of Alpha price is the central consideration for investors deciding whether to enter or hold a position. Noise in Alpha Stock price charts can make it difficult to distinguish meaningful trends from random fluctuations.Alpha Pro Related Equities
The following equities are related to Alpha Pro within the Industrials space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Alpha Pro 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 |
Alpha Pro Market Strength Events
Investors use market strength indicators for Alpha Pro to evaluate how the stock performs relative to broader market trends. These indicators support more precise timing of Alpha Pro Tech positions, helping investors maximize return and minimize poorly-timed trades.
Alpha Pro Risk Indicators
A careful analysis of Alpha Pro's basic risk indicators provides context for understanding the risk environment surrounding alpha stock. This understanding is an essential input for forecasting Alpha Pro's future price and for deciding how to manage the associated investment risk.
| Mean Deviation | 1.63 | |||
| Semi Deviation | 1.73 | |||
| Standard Deviation | 2.13 | |||
| Variance | 4.56 | |||
| Downside Variance | 3.48 | |||
| Semi Variance | 2.98 | |||
| Expected Short fall | -2.12 |
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 Alpha Pro
Coverage intensity for Alpha Pro Tech matters because narrative visibility can influence sentiment, participation, and volatility around the name. This is most useful when investors want to understand why a security is suddenly drawing more public discussion.
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More Resources for Alpha Stock Analysis
A structured review of Alpha Pro Tech begins with its financial statements and overall trends. These ratios help explain how earnings, efficiency, and value creation are connected. Values are derived from Alpha Pro's disclosed financial information. Supporting reports for Alpha Pro Tech Stock are presented below:Historical Fundamental Analysis of Alpha Pro offers a historical basis for evaluating projection assumptions about Alpha Pro. The view provides historical context for the projection set. Alpha Pro at P/E 10.04 and ROE 5.66% (39.72 Million market cap) - this analysis works best as a complementary layer when evaluating how the position fits in a broader portfolio. Those return and profitability levels shape the investment picture - the supplemental tools help investors decide if they are sustainable. You can also try the My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.