Pro Blend Mutual Fund Forward View - Triple Exponential Smoothing

MNHCX Fund  USD 23.85  -0.40  -1.65%   
The Triple Exponential Smoothing forecast reference data for Pro Blend Maximum Term is based on the equity's recent trading history. This page summarizes the model output and key accuracy metrics for reference.
The Triple Exponential Smoothing forecasted value of Pro Blend Maximum Term on the next trading day is expected to be 23.81 with a mean absolute deviation of 0.17 and the sum of the absolute errors of 10.30.As with simple exponential smoothing, in triple exponential smoothing models past Pro Blend observations are given exponentially smaller weights as the observations get older. In other words, recent observations are given relatively more weight in forecasting than the older Pro Blend Maximum Term observations. All Triple Exponential Smoothing forecast figures shown for Pro Blend Maximum Term are reference data reflecting model output based on available historical prices.
Triple exponential smoothing for Pro Blend - also known as the Winters method - is a refinement of the popular double exponential smoothing model with the addition of periodicity (seasonality) component. Simple exponential smoothing technique works best with data where there are no trend or seasonality components to the data. When Pro Blend 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 trend in Pro Blend price movement. However, neither of these exponential smoothing models address any seasonality of Pro Blend Maximum.

Triple Exponential Smoothing Price Forecast For the 19th of March

Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Pro Blend Maximum Term on the next trading day is expected to be 23.81 with a mean absolute deviation of 0.17 , mean absolute percentage error of 0.04 , and the sum of the absolute errors of 10.30 .
Please note that although there have been many attempts to predict Pro Mutual Fund 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 Pro Blend's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Mutual Fund Forecast Pattern

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Forecasted Value

Forecasting Pro Blend Maximum Term for the next session involves measuring the model's historical ability to define credible downside and upside scenarios. Used properly, these levels provide context around forecast dispersion rather than certainty about the next closing print.
Market Value
23.85
23.81
Expected Value
24.61
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Triple Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of Pro Blend mutual fund data series using in forecasting. Note that when a statistical model is used to represent Pro Blend mutual fund, 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 CriteriaHuge
BiasArithmetic mean of the errors 0.0431
MADMean absolute deviation0.1747
MAPEMean absolute percentage error0.0069
SAESum of the absolute errors10.3047
As with simple exponential smoothing, in triple exponential smoothing models past Pro Blend observations are given exponentially smaller weights as the observations get older. In other words, recent observations are given relatively more weight in forecasting than the older Pro Blend Maximum Term observations.

Other Forecasting Options for Pro Blend

Whether a novice or experienced investor, anyone considering Pro needs to understand the dynamics of Pro Blend's price movement. Price charts for Pro Mutual Fund contain a significant amount of noise that can distort investment decisions.

Pro Blend Related Equities

The following equities are related to Pro Blend within the Allocation--85%+ Equity space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Pro Blend 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

Pro Blend Market Strength Events

Analyzing market strength indicators for Pro Blend enables investors to understand how the mutual fund performs relative to overall market momentum. These indicators are valuable tools for identifying when to enter or exit positions in Pro Blend Maximum Term.

Pro Blend Risk Indicators

Identifying and analyzing Pro Blend's key risk indicators is a foundational step in projecting how its price may evolve. This process helps investors quantify the risk associated with Pro Blend's and decide how to manage it.
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 Pro Blend

A coverage review of Pro Blend Maximum Term helps investors see when the security is attracting above-average attention from contributors and market observers. 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.