Grayscale Future Of Etf Market Value
| GFOF Etf | USD 27.77 0.76 2.81% |
| Symbol | Grayscale |
The market value of Grayscale Future is measured differently than its book value, which is the value of Grayscale that is recorded on the company's balance sheet. Investors also form their own opinion of Grayscale Future's value that differs from its market value or its book value, called intrinsic value, which is Grayscale Future's 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 Grayscale Future's market value can be influenced by many factors that don't directly affect Grayscale Future's 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 Grayscale Future's value and its price as these two are different measures arrived at by different means. Investors typically determine if Grayscale Future is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Grayscale Future's 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.
Grayscale Future '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 Grayscale Future's 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 Grayscale Future.
| 11/01/2024 |
| 12/01/2024 |
If you would invest 0.00 in Grayscale Future on November 1, 2024 and sell it all today you would earn a total of 0.00 from holding Grayscale Future of or generate 0.0% return on investment in Grayscale Future over 30 days. Grayscale Future is related to or competes with Grayscale Digital, Valkyrie Bitcoin, Bitwise Crypto, VanEck Digital, and First Trust. The index is designed by Bloomberg Index Services Limited to consist of U.S More
Grayscale Future 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 Grayscale Future's 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 Grayscale Future of upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 3.18 | |||
| Information Ratio | 0.1569 | |||
| Maximum Drawdown | 23.9 | |||
| Value At Risk | (5.99) | |||
| Potential Upside | 6.72 |
Grayscale Future Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Grayscale Future's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Grayscale Future's standard deviation. In reality, there are many statistical measures that can use Grayscale Future historical prices to predict the future Grayscale Future's volatility.| Risk Adjusted Performance | 0.1535 | |||
| Jensen Alpha | 0.3625 | |||
| Total Risk Alpha | 0.0861 | |||
| Sortino Ratio | 0.2044 | |||
| Treynor Ratio | 0.2389 |
Grayscale Future Backtested Returns
Grayscale Future appears to be not too volatile, given 3 months investment horizon. Grayscale Future holds Efficiency (Sharpe) Ratio of 0.23, which attests that the entity had a 0.23% return per unit of standard deviation over the last 3 months. By evaluating Grayscale Future's technical indicators, you can evaluate if the expected return of 0.93% is justified by implied risk. Please utilize Grayscale Future's risk adjusted performance of 0.1535, and Market Risk Adjusted Performance of 0.2489 to validate if our risk estimates are consistent with your expectations. The etf retains a Market Volatility (i.e., Beta) of 3.26, which attests to a somewhat significant risk relative to the market. As the market goes up, the company is expected to outperform it. However, if the market returns are negative, Grayscale Future will likely underperform.
Auto-correlation | 0.37 |
Below average predictability
Grayscale Future of has below average predictability. Overlapping area represents the amount of predictability between Grayscale Future time series from 1st of November 2024 to 16th of November 2024 and 16th of November 2024 to 1st of December 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 Grayscale Future price movement. The serial correlation of 0.37 indicates that just about 37.0% of current Grayscale Future price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.37 | |
| Spearman Rank Test | 0.56 | |
| Residual Average | 0.0 | |
| Price Variance | 0.78 |
Grayscale Future lagged returns against current returns
Autocorrelation, which is Grayscale Future 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 Grayscale Future's etf expected returns. We can calculate the autocorrelation of Grayscale Future returns to help us make a trade decision. For example, suppose you find that Grayscale Future 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 |
Grayscale Future 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 Grayscale Future etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Grayscale Future etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Grayscale Future etf over time.
Current vs Lagged Prices |
| Timeline |
Grayscale Future Lagged Returns
When evaluating Grayscale Future's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Grayscale Future etf have on its future price. Grayscale Future 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, Grayscale Future autocorrelation shows the relationship between Grayscale Future etf current value and its past values and can show if there is a momentum factor associated with investing in Grayscale Future of.
Regressed Prices |
| Timeline |
Currently Active Assets on Macroaxis
When determining whether Grayscale Future is a strong investment it is important to analyze Grayscale Future's competitive position within its industry, examining market share, product or service uniqueness, and competitive advantages. Beyond financials and market position, potential investors should also consider broader economic conditions, industry trends, and any regulatory or geopolitical factors that may impact Grayscale Future's future performance. For an informed investment choice regarding Grayscale Etf, refer to the following important reports:Check out Grayscale Future Correlation, Grayscale Future Volatility and Grayscale Future Alpha and Beta module to complement your research on Grayscale Future. You can also try the Portfolio Suggestion module to get suggestions outside of your existing asset allocation including your own model portfolios.
Grayscale Future 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.