GOLDMAN SACHS Mutual Fund Forward View - Simple Exponential Smoothing
| GCSUX Fund | USD 29.83 -0.49 -1.62% |
This page documents Simple Exponential Smoothing forecast output for Goldman Sachs Small as reference data. The model is applied to historical closing prices and the resulting projection and error statistics are shown below. Key metrics including projected price and mean absolute deviation are summarized below. The reference data on this page covers both forecast levels and error statistics.
The Simple Exponential Smoothing forecasted value of Goldman Sachs Small on the next trading day is expected to be 29.85 with a mean absolute deviation of 0.30 and the sum of the absolute errors of 18.56.This simple exponential smoothing model begins by setting Goldman Sachs Small forecast for the second period equal to the observation of the first period. In other words, recent GOLDMAN SACHS observations are given relatively more weight in forecasting than the older observations. GOLDMAN SACHS's Simple Exponential Smoothing reference values are drawn from available trading data and are presented for informational reference only. Simple Exponential Smoothing Price Forecast For the 28th of March
Given 90 days horizon, the Simple Exponential Smoothing forecasted value of Goldman Sachs Small on the next trading day is expected to be 29.85 with a mean absolute deviation of 0.30 , mean absolute percentage error of 0.14 , and the sum of the absolute errors of 18.56 .Please note that although there have been many attempts to predict GOLDMAN 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 GOLDMAN SACHS'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 Goldman Sachs Small for the next session involves measuring the model's historical ability to define credible downside and upside scenarios. 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 GOLDMAN SACHS mutual fund data series using in forecasting. Note that when a statistical model is used to represent GOLDMAN SACHS 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 | 116.1419 |
| Bias | Arithmetic mean of the errors | -0.0056 |
| MAD | Mean absolute deviation | 0.3043 |
| MAPE | Mean absolute percentage error | 0.0099 |
| SAE | Sum of the absolute errors | 18.5634 |
Other Forecasting Options for GOLDMAN SACHS
MACD analysis of GOLDMAN tracks the relationship between two exponential moving averages of GOLDMAN SACHS's price. Many GOLDMAN SACHS's traders use Fibonacci levels to set entry and exit targets based on prior price swings. Average True Range measures the typical daily price swing for GOLDMAN, accounting for gaps. The frequency and magnitude of gaps reveal how much new information is being priced into GOLDMAN outside regular hours.GOLDMAN SACHS Related Equities
Sizing up GOLDMAN SACHS against these stocks within the Small Blend space shows how it compares on key financial measures. Growth rate gaps between GOLDMAN SACHS and its peers often explain pricing differences in the market. Firms that trade at big discounts to peers on core metrics may be worth more research. This type of review is most useful when done often to track how positions shift over time.
| Risk & Return | Correlation |
GOLDMAN SACHS Market Strength Events
Market strength indicators for GOLDMAN SACHS assess how the mutual fund responds to changes in investor sentiment. These signals support informed decisions about when to enter or exit Goldman Sachs Small positions. Market strength signals help investors time Goldman Sachs Small positions with greater precision and confidence. These tools add market timing discipline when analyzing GOLDMAN SACHS mutual fund.
| Rate Of Daily Change | 0.98 | |||
| Day Median Price | 29.83 | |||
| Day Typical Price | 29.83 | |||
| Price Action Indicator | -0.25 | |||
| Period Momentum Indicator | -0.49 | |||
| Relative Strength Index | 46.28 |
GOLDMAN SACHS Risk Indicators
Risk indicator analysis for GOLDMAN SACHS is a critical component of accurate price forecasting. Identifying and quantifying the risks associated with GOLDMAN SACHS's allows investors to make better-informed decisions. Understanding GOLDMAN SACHS's risk indicators is a fundamental step in managing investment exposure responsibly. Understanding the risk embedded in GOLDMAN SACHS's allows investors to decide whether to accept, reduce, or hedge exposure.
| Mean Deviation | 0.9509 | |||
| Semi Deviation | 1.27 | |||
| Standard Deviation | 1.22 | |||
| Variance | 1.48 | |||
| Downside Variance | 1.76 | |||
| Semi Variance | 1.63 | |||
| Expected Short fall | -0.91 |
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 Small can signal where market attention is concentrating at the moment. A disciplined read of coverage separates durable relevance from temporary noise.
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.