STATE STREET Mutual Fund Forward View - Triple Exponential Smoothing

SSBWX Fund  USD 13.93  -0.03  -0.21%   
STATE STREET's Triple Exponential Smoothing reference data is presented on this page, derived from the application of the forecasting model to historical closing prices. Projected values and accuracy measures are included for reference.
The Triple Exponential Smoothing forecasted value of State Street Target on the next trading day is expected to be 13.91 with a mean absolute deviation of 0.06 and the sum of the absolute errors of 3.67.As with simple exponential smoothing, in triple exponential smoothing models past STATE STREET 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 State Street Target observations. The Triple Exponential Smoothing reference information for STATE STREET is based on available price data and is intended for informational purposes.
Triple exponential smoothing for STATE STREET - 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 STATE STREET 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 STATE STREET price movement. However, neither of these exponential smoothing models address any seasonality of State Street Target.

Triple Exponential Smoothing Price Forecast For the 20th of March

Given 90 days horizon, the Triple Exponential Smoothing forecasted value of State Street Target on the next trading day is expected to be 13.91 with a mean absolute deviation of 0.06 , mean absolute percentage error of 0.01 , and the sum of the absolute errors of 3.67 .
Please note that although there have been many attempts to predict STATE 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 STATE STREET'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 State Street Target for the next session involves measuring the model's historical ability to define credible downside and upside scenarios. At the moment, the model places downside around 13.25 and upside around 14.58 for the forecasting period.
Market Value
13.93
13.91
Expected Value
14.58
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 STATE STREET mutual fund data series using in forecasting. Note that when a statistical model is used to represent STATE STREET 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 0.0165
MADMean absolute deviation0.0622
MAPEMean absolute percentage error0.0044
SAESum of the absolute errors3.672
As with simple exponential smoothing, in triple exponential smoothing models past STATE STREET 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 State Street Target observations.

Other Forecasting Options for STATE STREET

For any investor considering STATE, STATE STREET's price movement is the central factor in determining investment viability. The noise present in STATE Mutual Fund price charts can distort investment decisions if not properly addressed.

STATE STREET Related Equities

The following equities are related to STATE STREET within the Target-Date 2030 space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing STATE STREET 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

STATE STREET Market Strength Events

Market strength indicators for STATE STREET mutual fund help investors evaluate the security's behavior relative to ongoing market conditions. These tools support better market timing and help identify entry and exit signals for State Street Target.

STATE STREET Risk Indicators

The analysis of STATE STREET's basic risk indicators is a key input for accurate price forecasting and sound investment decisions. Understanding the risk in STATE STREET's investment allows investors to make informed choices about accepting or mitigating that 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 STATE STREET

A coverage review of State Street Target shows when the security is attracting above-average attention from contributors and market observers. The practical risk is that faster visibility can increase both interest and skepticism at the same time.

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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.