Goldman Sachs Etf Forward View - Double Exponential Smoothing
| GSUS Etf | USD 90.17 -0.39 -0.43% |
This page documents Double Exponential Smoothing forecast output for Goldman Sachs MarketBeta 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 Double Exponential Smoothing forecasted value of Goldman Sachs MarketBeta on the next trading day is expected to be 89.95 with a mean absolute deviation of 0.59 and the sum of the absolute errors of 35.07.When Goldman Sachs MarketBeta 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 Goldman Sachs MarketBeta 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 Goldman Sachs observations are given relatively more weight in forecasting than the older observations. Goldman Sachs's Double Exponential Smoothing reference values are drawn from available trading data and are presented for informational reference only. Double Exponential Smoothing Price Forecast For the 26th of March
Given 90 days horizon, the Double Exponential Smoothing forecasted value of Goldman Sachs MarketBeta on the next trading day is expected to be 89.95 with a mean absolute deviation of 0.59 , mean absolute percentage error of 0.55 , and the sum of the absolute errors of 35.07 .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
Forecasting Goldman Sachs MarketBeta for the next session involves measuring the model's historical ability to define credible downside and upside scenarios. The projected forecast band currently runs from roughly 89.17 on the downside to about 90.73 on the 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 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 | Huge |
| Bias | Arithmetic mean of the errors | -0.0677 |
| MAD | Mean absolute deviation | 0.5945 |
| MAPE | Mean absolute percentage error | 0.0063 |
| SAE | Sum of the absolute errors | 35.0741 |
Other Forecasting Options for Goldman Sachs
MACD analysis of Goldman tracks the relationship between two exponential moving averages of Goldman Sachs' price. Many Goldman Sachs' 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 Large 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.
| Risk & Return | Correlation |
Goldman Sachs Market Strength Events
Market strength indicators for Goldman Sachs assess how the etf responds to changes in investor sentiment. These signals support informed decisions about when to enter or exit Goldman Sachs MarketBeta positions. Market strength signals help investors time Goldman Sachs MarketBeta positions with greater precision and confidence. These tools add market timing discipline when analyzing Goldman Sachs etf.
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' allows investors to make better-informed decisions. Understanding Goldman Sachs' risk indicators is a fundamental step in managing investment exposure responsibly. Understanding the risk embedded in Goldman Sachs' allows investors to decide whether to accept, reduce, or hedge exposure.
| Mean Deviation | 0.6083 | |||
| Standard Deviation | 0.7791 | |||
| Variance | 0.6071 |
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
A coverage review of Goldman Sachs MarketBeta shows when the security is attracting above-average attention from contributors and market observers. A disciplined read of coverage separates durable relevance from temporary noise.
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More Resources for Goldman Etf Analysis
Analysis of Goldman Sachs MarketBeta often begins with its financial statements and historical patterns. Selected reports below provide context for Goldman Etf:Historical Fundamental Analysis of Goldman Sachs offers a historical basis for evaluating projection assumptions about Goldman Sachs. This analysis of Goldman Sachs works best as a complementary layer when evaluating how the security fits in a broader portfolio. Goldman Sachs analysis across multiple dimensions - risk, valuation, diversification - produces a more informed position-sizing decision. You can also try the Portfolio Dashboard module to portfolio dashboard that provides centralized access to all your investments.
Comparing Goldman Sachs' market price with book value reveals how market sentiment relates to accounting fundamentals. Intrinsic value represents an estimate of underlying worth and can differ from both market price and book value.
Goldman Sachs' value is shaped by fundamental inputs, whereas price is shaped by supply and demand dynamics. The actual Goldman Sachs transaction price is determined by real-time order flow on the exchange.