Fidelity Metaverse Etf Market Value
| FMET Etf | USD 30.40 0.20 0.65% |
| Symbol | Fidelity |
The market value of Fidelity Metaverse ETF is measured differently than its book value, which is the value of Fidelity that is recorded on the company's balance sheet. Investors also form their own opinion of Fidelity Metaverse's value that differs from its market value or its book value, called intrinsic value, which is Fidelity Metaverse'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 Fidelity Metaverse's market value can be influenced by many factors that don't directly affect Fidelity Metaverse'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 Fidelity Metaverse's value and its price as these two are different measures arrived at by different means. Investors typically determine if Fidelity Metaverse is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Fidelity Metaverse'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.
Fidelity Metaverse '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 Fidelity Metaverse'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 Fidelity Metaverse.
| 05/08/2023 |
| 11/28/2024 |
If you would invest 0.00 in Fidelity Metaverse on May 8, 2023 and sell it all today you would earn a total of 0.00 from holding Fidelity Metaverse ETF or generate 0.0% return on investment in Fidelity Metaverse over 570 days. Fidelity Metaverse is related to or competes with Fidelity Crypto, Fidelity Covington, ProShares Metaverse, Fidelity Covington, and Fidelity Covington. The fund normally invests at least 80 percent of assets in securities included in the index and in depositary receipts r... More
Fidelity Metaverse 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 Fidelity Metaverse'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 Fidelity Metaverse ETF upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.26 | |||
| Information Ratio | (0.06) | |||
| Maximum Drawdown | 4.84 | |||
| Value At Risk | (2.05) | |||
| Potential Upside | 1.71 |
Fidelity Metaverse Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Fidelity Metaverse's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Fidelity Metaverse's standard deviation. In reality, there are many statistical measures that can use Fidelity Metaverse historical prices to predict the future Fidelity Metaverse's volatility.| Risk Adjusted Performance | 0.0407 | |||
| Jensen Alpha | (0.05) | |||
| Total Risk Alpha | (0.14) | |||
| Sortino Ratio | (0.06) | |||
| Treynor Ratio | 0.0591 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Fidelity Metaverse's 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.
Fidelity Metaverse ETF Backtested Returns
Currently, Fidelity Metaverse ETF is very steady. Fidelity Metaverse ETF secures Sharpe Ratio (or Efficiency) of 0.0429, which denotes the etf had a 0.0429% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Fidelity Metaverse ETF, which you can use to evaluate the volatility of the entity. Please confirm Fidelity Metaverse's Mean Deviation of 0.8675, downside deviation of 1.26, and Coefficient Of Variation of 2052.44 to check if the risk estimate we provide is consistent with the expected return of 0.0494%. The etf shows a Beta (market volatility) of 0.79, which means possible diversification benefits within a given portfolio. As returns on the market increase, Fidelity Metaverse's returns are expected to increase less than the market. However, during the bear market, the loss of holding Fidelity Metaverse is expected to be smaller as well.
Auto-correlation | 0.10 |
Insignificant predictability
Fidelity Metaverse ETF has insignificant predictability. Overlapping area represents the amount of predictability between Fidelity Metaverse time series from 8th of May 2023 to 17th of February 2024 and 17th of February 2024 to 28th of November 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 Fidelity Metaverse ETF price movement. The serial correlation of 0.1 indicates that less than 10.0% of current Fidelity Metaverse price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.1 | |
| Spearman Rank Test | 0.25 | |
| Residual Average | 0.0 | |
| Price Variance | 1.03 |
Fidelity Metaverse ETF lagged returns against current returns
Autocorrelation, which is Fidelity Metaverse 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 Fidelity Metaverse's etf expected returns. We can calculate the autocorrelation of Fidelity Metaverse returns to help us make a trade decision. For example, suppose you find that Fidelity Metaverse 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 |
Fidelity Metaverse 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 Fidelity Metaverse etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Fidelity Metaverse etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Fidelity Metaverse etf over time.
Current vs Lagged Prices |
| Timeline |
Fidelity Metaverse Lagged Returns
When evaluating Fidelity Metaverse's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Fidelity Metaverse etf have on its future price. Fidelity Metaverse 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, Fidelity Metaverse autocorrelation shows the relationship between Fidelity Metaverse etf current value and its past values and can show if there is a momentum factor associated with investing in Fidelity Metaverse ETF.
Regressed Prices |
| Timeline |
Thematic Opportunities
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Check out Fidelity Metaverse Correlation, Fidelity Metaverse Volatility and Fidelity Metaverse Alpha and Beta module to complement your research on Fidelity Metaverse. You can also try the Stocks Directory module to find actively traded stocks across global markets.
Fidelity Metaverse 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.