Applied Finance Mutual Fund Forward View - Double Exponential Smoothing

AFDVX Fund  USD 22.80  -0.42  -1.81%   
This reference view applies Double Exponential Smoothing to Applied Finance Explorer's historical closing prices. Applied Finance Explorer's Double Exponential Smoothing reference page summarizes the forecasted price and model accuracy metrics from daily trading data.
The Double Exponential Smoothing forecasted value of Applied Finance Explorer on the next trading day is expected to be 22.74 with a mean absolute deviation of 0.18 and the sum of the absolute errors of 10.48.When Applied Finance Explorer 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 Applied Finance Explorer 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 Applied Finance observations are given relatively more weight in forecasting than the older observations. All forecast values on this page for Applied Finance Explorer are Double Exponential Smoothing reference data derived from historical price series.
Double exponential smoothing - also known as Holt exponential smoothing is a refinement of the popular simple exponential smoothing model with an additional trending component. Double exponential smoothing model for Applied Finance works best with periods where there are trends or seasonality.

Double Exponential Smoothing Price Forecast For the 24th of March

Given 90 days horizon, the Double Exponential Smoothing forecasted value of Applied Finance Explorer on the next trading day is expected to be 22.74 with a mean absolute deviation of 0.18 , mean absolute percentage error of 0.05 , and the sum of the absolute errors of 10.48 .
Please note that although there have been many attempts to predict Applied 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 Applied Finance'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

The next-day forecast for Applied Finance Explorer focuses on identifying predictive downside and upside bands that can frame a realistic trading range. At the moment, the model places downside around 21.79 and upside around 23.69 for the forecasting period.
Market Value
22.80
22.74
Expected Value
23.69
Upside

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 Applied Finance mutual fund data series using in forecasting. Note that when a statistical model is used to represent Applied Finance 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 0.0438
MADMean absolute deviation0.1775
MAPEMean absolute percentage error0.0075
SAESum of the absolute errors10.4752
When Applied Finance Explorer 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 Applied Finance Explorer 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 Applied Finance observations are given relatively more weight in forecasting than the older observations.

Other Forecasting Options for Applied Finance

Volume-weighted price analysis for Applied Mutual Fund gives heavier weight to price levels where trading activity was highest. Crossovers in the MACD line and signal line can identify shifts in Applied momentum before they appear in raw price.

Applied Finance Related Equities

Applied Finance's market space within the Small Value space is best grasped by looking at the firms listed below. Key comparison metrics include price-to-earnings, profit margin, and revenue growth across Applied Finance's peer group. Firms that trade at big discounts to peers on core metrics may be worth more research.
 Risk & Return  Correlation

Applied Finance Market Strength Events

Evaluating the market strength of Applied Finance mutual fund allows investors to gauge shifts in market momentum. By monitoring these indicators, investors can identify the most opportune moments to trade Applied Finance Explorer.

Applied Finance Risk Indicators

Understanding Applied Finance's risk indicators is essential for any investor seeking to forecast its future price accurately. By identifying how much risk is embedded in Applied Finance's investment, investors can decide how to position their exposure.
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 Applied Finance

Coverage intensity for Applied Finance Explorer 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.

Other Macroaxis Stories

Macroaxis story coverage is designed for a broad investing audience that ranges from self-directed traders to advisers, researchers, and institutional market participants. The content is intended to support people who want a more structured path from headline information to portfolio action.