Oppenheimer Roc Mutual Fund Forward View - Double Exponential Smoothing

OCACX Fund  USD 7.85  0.01  0.13%   
The Double Exponential Smoothing forecast reference data for Oppenheimer Roc Ca is based on the equity's recent trading history. This page summarizes the model output and key accuracy metrics for reference.
The Double Exponential Smoothing forecasted value of Oppenheimer Roc Ca on the next trading day is expected to be 7.85 with a mean absolute deviation of 0.01 and the sum of the absolute errors of 0.54.When Oppenheimer Roc Ca 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 Oppenheimer Roc Ca 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 Oppenheimer Roc observations are given relatively more weight in forecasting than the older observations. All Double Exponential Smoothing forecast figures shown for Oppenheimer Roc Ca are reference data reflecting model output based on available historical prices.
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 Oppenheimer Roc 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 Oppenheimer Roc Ca on the next trading day is expected to be 7.85 with a mean absolute deviation of 0.01 , mean absolute percentage error of 0.0002 , and the sum of the absolute errors of 0.54 .
Please note that although there have been many attempts to predict Oppenheimer 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 Oppenheimer Roc'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

This next-day forecast for Oppenheimer Roc Ca uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. At the moment, the model places downside around 7.67 and upside around 8.03 for the forecasting period.
Market Value
7.85
7.85
Expected Value
8.03
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 Oppenheimer Roc mutual fund data series using in forecasting. Note that when a statistical model is used to represent Oppenheimer Roc 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 -0.0013
MADMean absolute deviation0.009
MAPEMean absolute percentage error0.0011
SAESum of the absolute errors0.54
When Oppenheimer Roc Ca 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 Oppenheimer Roc Ca 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 Oppenheimer Roc observations are given relatively more weight in forecasting than the older observations.

Other Forecasting Options for Oppenheimer Roc

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

Oppenheimer Roc Related Equities

The following equities are related to Oppenheimer Roc within the Muni California Long space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Oppenheimer Roc 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

Oppenheimer Roc Market Strength Events

Analyzing market strength indicators for Oppenheimer Roc 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 Oppenheimer Roc Ca.

Oppenheimer Roc Risk Indicators

Identifying and analyzing Oppenheimer Roc's key risk indicators is a foundational step in projecting how its price may evolve. This process helps investors quantify the risk associated with Oppenheimer Roc'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 Oppenheimer Roc

Story coverage around Oppenheimer Roc Ca often expands when market conditions, narrative momentum, or risk-adjusted performance make the security more visible to investors. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.

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.