FIDELITY LARGE Mutual Fund Forward View - Double Exponential Smoothing

FLCSX Fund  USD 65.15  0.31  0.48%   
This page provides reference data for FIDELITY LARGE using Double Exponential Smoothing forecasting. The projected value and error metrics are calculated from available daily price observations.
The Double Exponential Smoothing forecasted value of Fidelity Large Cap on the next trading day is expected to be 65.07 with a mean absolute deviation of 0.41 and the sum of the absolute errors of 24.47.When Fidelity Large Cap 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 Fidelity Large Cap 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 FIDELITY LARGE observations are given relatively more weight in forecasting than the older observations. This Double Exponential Smoothing reference page for FIDELITY LARGE presents model-generated projections from historical price data for informational purposes.
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 FIDELITY LARGE works best with periods where there are trends or seasonality.

Double Exponential Smoothing Price Forecast For the 19th of March

Given 90 days horizon, the Double Exponential Smoothing forecasted value of Fidelity Large Cap on the next trading day is expected to be 65.07 with a mean absolute deviation of 0.41 , mean absolute percentage error of 0.28 , and the sum of the absolute errors of 24.47 .
Please note that although there have been many attempts to predict FIDELITY 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 FIDELITY LARGE'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

For the next trading day, Macroaxis evaluates FIDELITY LARGE's predictive range by looking for statistically meaningful downside and upside boundaries. Used properly, these levels provide context around forecast dispersion rather than certainty about the next closing print.
Market Value
65.15
65.07
Expected Value
65.83
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 FIDELITY LARGE mutual fund data series using in forecasting. Note that when a statistical model is used to represent FIDELITY LARGE 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 0.1019
MADMean absolute deviation0.4079
MAPEMean absolute percentage error0.0061
SAESum of the absolute errors24.4732
When Fidelity Large Cap 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 Fidelity Large Cap 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 FIDELITY LARGE observations are given relatively more weight in forecasting than the older observations.

Other Forecasting Options for FIDELITY LARGE

For investors considering FIDELITY, FIDELITY LARGE's price movement is the most direct driver of investment returns. Noise in FIDELITY Mutual Fund price charts can make identifying meaningful trends difficult without dedicated analytical tools.

FIDELITY LARGE Related Equities

The following equities are related to FIDELITY LARGE within the Large Blend space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing FIDELITY LARGE 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

FIDELITY LARGE Market Strength Events

Market strength indicators for FIDELITY LARGE provide investors with a view of how the mutual fund performs across different market environments. By analyzing these indicators, traders can determine the best moments to enter or exit positions in Fidelity Large Cap.

FIDELITY LARGE Risk Indicators

A structured analysis of FIDELITY LARGE's risk indicators is one of the most reliable ways to improve the accuracy of price forecasts. Understanding the risk embedded in FIDELITY LARGE's allows investors to decide whether to accept, reduce, or hedge 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 FIDELITY LARGE

A coverage review of Fidelity Large Cap helps investors see when the security is attracting above-average attention from contributors and market observers. A disciplined read of coverage helps investors separate durable relevance from temporary noise.

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.