Goldman Sachs Activebeta Etf Market Value
| GSLC Etf | USD 98.90 0.61 0.61% |
| Symbol | Goldman |
The market value of Goldman Sachs ActiveBeta is measured differently than its book value, which is the value of Goldman that is recorded on the company's balance sheet. Investors also form their own opinion of Goldman Sachs' value that differs from its market value or its book value, called intrinsic value, which is Goldman Sachs' true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Goldman Sachs' market value can be influenced by many factors that don't directly affect Goldman Sachs' underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Goldman Sachs' value and its price as these two are different measures arrived at by different means. Investors typically determine if Goldman Sachs is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Goldman Sachs' price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.
Goldman Sachs 'What if' Analysis
In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to Goldman Sachs' etf what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of Goldman Sachs.
| 03/19/2024 |
| 04/18/2024 |
If you would invest 0.00 in Goldman Sachs on March 19, 2024 and sell it all today you would earn a total of 0.00 from holding Goldman Sachs ActiveBeta or generate 0.0% return on investment in Goldman Sachs over 30 days. Goldman Sachs is related to or competes with Dimensional Targeted, Dimensional World, Dimensional Small, and Dimensional Equity. The index is designed to deliver exposure to equity securities of large capitalization U.S More
Goldman Sachs Upside/Downside Indicators
Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure Goldman Sachs' etf current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess Goldman Sachs ActiveBeta upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.6899 | |||
| Information Ratio | 0.0342 | |||
| Maximum Drawdown | 3.53 | |||
| Value At Risk | (1.25) | |||
| Potential Upside | 1.14 |
Goldman Sachs Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Goldman Sachs' investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Goldman Sachs' standard deviation. In reality, there are many statistical measures that can use Goldman Sachs historical prices to predict the future Goldman Sachs' volatility.| Risk Adjusted Performance | 0.0705 | |||
| Jensen Alpha | 0.0248 | |||
| Total Risk Alpha | 0.0175 | |||
| Sortino Ratio | 0.0365 | |||
| Treynor Ratio | 0.0701 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Goldman Sachs' price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
Goldman Sachs ActiveBeta Backtested Returns
We consider Goldman Sachs very steady. Goldman Sachs ActiveBeta holds Efficiency (Sharpe) Ratio of 0.0898, which attests that the entity had a 0.0898% return per unit of risk over the last 3 months. We have found thirty technical indicators for Goldman Sachs ActiveBeta, which you can use to evaluate the volatility of the entity. Please check out Goldman Sachs' Downside Deviation of 0.6899, market risk adjusted performance of 0.0801, and Risk Adjusted Performance of 0.0705 to validate if the risk estimate we provide is consistent with the expected return of 0.0655%. The etf retains a Market Volatility (i.e., Beta) of 1.01, which attests to a somewhat significant risk relative to the market. Goldman Sachs returns are very sensitive to returns on the market. As the market goes up or down, Goldman Sachs is expected to follow.
Auto-correlation | 0.18 |
Very weak predictability
Goldman Sachs ActiveBeta has very weak predictability. Overlapping area represents the amount of predictability between Goldman Sachs time series from 19th of March 2024 to 3rd of April 2024 and 3rd of April 2024 to 18th of April 2024. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Goldman Sachs ActiveBeta price movement. The serial correlation of 0.18 indicates that over 18.0% of current Goldman Sachs price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.18 | |
| Spearman Rank Test | 0.06 | |
| Residual Average | 0.0 | |
| Price Variance | 1.95 |
Goldman Sachs ActiveBeta lagged returns against current returns
Autocorrelation, which is Goldman Sachs etf's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting Goldman Sachs' etf expected returns. We can calculate the autocorrelation of Goldman Sachs returns to help us make a trade decision. For example, suppose you find that Goldman Sachs has exhibited high autocorrelation historically, and you observe that the etf is moving up for the past few days. In that case, you can expect the price movement to match the lagging time series.
Current and Lagged Values |
| Timeline |
Goldman Sachs regressed lagged prices vs. current prices
Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If Goldman Sachs etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Goldman Sachs etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Goldman Sachs etf over time.
Current vs Lagged Prices |
| Timeline |
Goldman Sachs Lagged Returns
When evaluating Goldman Sachs' market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Goldman Sachs etf have on its future price. Goldman Sachs autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, Goldman Sachs autocorrelation shows the relationship between Goldman Sachs etf current value and its past values and can show if there is a momentum factor associated with investing in Goldman Sachs ActiveBeta.
Regressed Prices |
| Timeline |
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards Goldman Sachs in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, Goldman Sachs' short interest history, or implied volatility extrapolated from Goldman Sachs options trading.
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Goldman Sachs technical etf analysis exercises models and trading practices based on price and volume transformations, such as the moving averages, relative strength index, regressions, price and return correlations, business cycles, etf market cycles, or different charting patterns.