Goldman Sachs ETF Forward View - Simple Moving Average
| JUST ETF | USD 91.84 -1.40 -1.50% |
The Simple Moving Average forecast shown here for Goldman Sachs is reference data produced from its historical price series. The projected value and error measures below serve as reference information.
The Simple Moving Average forecasted value of Goldman Sachs JUST on the next trading day is expected to be 91.84 with a mean absolute deviation of 0.63 and the sum of the absolute errors of 37.75.The simple moving average model is conceptually a linear regression of the current value of Goldman Sachs JUST price series against current and previous (unobserved) value of Goldman Sachs. In time series analysis, the simple moving-average model is a very common approach for modeling univariate price series models including forecasting prices into the future This Simple Moving Average reference page for Goldman Sachs presents model-generated projections from historical price data for informational purposes. Simple Moving Average Price Forecast For the 28th of March
Given 90 days horizon, the Simple Moving Average forecasted value of Goldman Sachs JUST on the next trading day is expected to be 91.84 with a mean absolute deviation of 0.63 , mean absolute percentage error of 0.63 , and the sum of the absolute errors of 37.75 .Please note that although there have been many attempts to predict Goldman ETF 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 Goldman Sachs' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
ETF Forecast Pattern
| Backtest Goldman Sachs | Goldman Sachs Price Prediction | Research Analysis |
Forecasted Value
For the next trading day, Macroaxis evaluates Goldman Sachs' predictive range by looking for statistically meaningful downside and upside boundaries. No forecasting approach has been shown to beat all others over time. Investors should treat any model output as a guide, not a guarantee.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Simple Moving Average forecasting method's relative quality and the estimations of the prediction error of Goldman Sachs ETF data series using in forecasting. Note that when a statistical model is used to represent Goldman Sachs ETF, 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 | 115.8106 |
| Bias | Arithmetic mean of the errors | 0.1297 |
| MAD | Mean absolute deviation | 0.6292 |
| MAPE | Mean absolute percentage error | 0.0066 |
| SAE | Sum of the absolute errors | 37.75 |
Other Forecasting Options for Goldman Sachs
The distribution of Goldman Sachs' daily returns is typically non-normal, with fatter tails than a Gaussian model predicts. This can reveal hidden support and resistance zones in Goldman Sachs' chart that simple price charts miss.Goldman Sachs Related Equities
The stocks listed below are peers of Goldman Sachs within the Large Blend space and offer context for ranking and strength. Growth rate gaps between Goldman Sachs and its peers often explain pricing differences in the market.
| Risk & Return | Correlation |
Goldman Sachs Market Strength Events
Market strength indicators for Goldman Sachs give insight into the ETF's responsiveness to broader forces. These indicators are useful for traders seeking optimal timing for positions in Goldman Sachs JUST.
Goldman Sachs Risk Indicators
A thorough review of Goldman Sachs' risk indicators is an important first step in forecasting its price. Quantifying the risk involved in Goldman Sachs' allows investors to make better decisions about entry, sizing, and hedging.
| Mean Deviation | 0.6068 | |||
| Standard Deviation | 0.786 | |||
| Variance | 0.6179 |
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 Goldman Sachs
The amount of media and story coverage tied to Goldman Sachs JUST can signal where market attention is concentrating at the moment. Used properly, this context can help investors judge whether visibility is reinforcing the thesis or attracting more speculative pressure.
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.
Story Categories
Currently Trending Categories
More Resources for Goldman ETF Analysis
The foundation for reviewing Goldman Sachs JUST is its fund data, holdings, and performance history. These measures help explain how the fund delivers returns and manages investor costs.Cross-verify projections for Goldman Sachs using Historical Fundamental Analysis of Goldman Sachs. Goldman Sachs information on this page supports broader portfolio research rather than acting as a stand-alone signal. Checking Goldman Sachs against category peers and portfolio fit tools below produces a more complete investment picture. You can also try the Portfolio Diagnostics module to use generated alerts and portfolio events aggregator to diagnose current holdings.
NAV captures Goldman portfolio value, while market price captures the collective view of trading participants. All values are presented as reference data.
Goldman Sachs NAV depends on underlying asset values, while price depends on secondary market activity. Fund-level metrics such as tracking difference and expense ratio add depth to the analysis.