State Street Mutual Fund Forward View - Simple Exponential Smoothing
| SIVIX Fund | USD 14.64 0.10 0.69% |
The Simple Exponential Smoothing forecast shown here for State Street is reference data produced from its historical price series. The projected value and error measures below serve as reference information. This data is provided for reference and analytical review. The Simple Exponential Smoothing output serves as one input among many for analytical review.
The Simple Exponential Smoothing forecasted value of State Street Institutional on the next trading day is expected to be 14.64 with a mean absolute deviation of 0.12 and the sum of the absolute errors of 7.62.This simple exponential smoothing model begins by setting State Street Institutional forecast for the second period equal to the observation of the first period. In other words, recent State Street observations are given relatively more weight in forecasting than the older observations. This Simple Exponential Smoothing reference page for State Street presents model-generated projections from historical price data for informational purposes. Simple Exponential Smoothing Price Forecast For the 26th of March
Given 90 days horizon, the Simple Exponential Smoothing forecasted value of State Street Institutional on the next trading day is expected to be 14.64 with a mean absolute deviation of 0.12 , mean absolute percentage error of 0.03 , and the sum of the absolute errors of 7.62 .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
| Backtest State Street | State Street Price Prediction | Research Analysis |
Forecasted Value
This next-day forecast for State Street Institutional uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. Used properly, these levels provide context around forecast dispersion rather than certainty about the next closing print.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Simple 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.| AIC | Akaike Information Criteria | 114.4749 |
| Bias | Arithmetic mean of the errors | 0.0077 |
| MAD | Mean absolute deviation | 0.125 |
| MAPE | Mean absolute percentage error | 0.0082 |
| SAE | Sum of the absolute errors | 7.6239 |
Other Forecasting Options for State Street
The distribution of State Street's daily returns is typically non-normal, with fatter tails than a Gaussian model predicts. This can reveal hidden support and resistance zones in State Street's chart that simple price charts miss. The slope of State Street's linear regression channel quantifies trend direction and strength over a chosen lookback period. Divergences between OBV and price can foreshadow trend changes in State.State Street Related Equities
The peer firms below within the Small Blend space can help frame State Street's pricing and running costs in context. Revenue and margin checks across this group help investors set expectations for State Street's results. When State Street breaks from its peer group on a key metric, it often signals a firm-level change worth exploring.
| Risk & Return | Correlation |
State Street Market Strength Events
Market strength indicators for State Street give insight into the mutual fund's responsiveness to broader forces. These indicators are useful for traders seeking optimal timing for positions in State Street Institutional. Market strength analysis for State Street Institutional works best when combined with volume and volatility data. For State Street, strength indicators are a practical complement to price and fundamental analysis.
| Rate Of Daily Change | 1.01 | |||
| Day Median Price | 14.64 | |||
| Day Typical Price | 14.64 | |||
| Price Action Indicator | 0.05 | |||
| Period Momentum Indicator | 0.1 | |||
| Relative Strength Index | 42.96 |
State Street Risk Indicators
A thorough review of State Street's risk indicators is an important first step in forecasting its price. Quantifying the risk involved in State Street's allows investors to make better decisions about entry, sizing, and hedging. The assessment of State Street's risk indicators plays a key role in managing investment exposure. Identifying the magnitude of risk in State Street's provides context to choose between accepting or hedging exposure.
| Mean Deviation | 0.8033 | |||
| Standard Deviation | 1.05 | |||
| Variance | 1.11 |
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
Story coverage around State Street Institutional often expands when market conditions, narrative momentum, or risk-adjusted performance make the security more visible to investors. 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.