Fidelity Value Factor Etf Market Value
| FVAL Etf | USD 54.72 0.99 1.78% |
| Symbol | Fidelity |
The market value of Fidelity Value Factor 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 Value's value that differs from its market value or its book value, called intrinsic value, which is Fidelity Value'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 Value's market value can be influenced by many factors that don't directly affect Fidelity Value'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 Value's value and its price as these two are different measures arrived at by different means. Investors typically determine if Fidelity Value is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Fidelity Value'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 Value '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 Value'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 Value.
| 05/01/2023 |
| 04/25/2024 |
If you would invest 0.00 in Fidelity Value on May 1, 2023 and sell it all today you would earn a total of 0.00 from holding Fidelity Value Factor or generate 0.0% return on investment in Fidelity Value over 360 days. Fidelity Value is related to or competes with Hartford Multifactor, Hartford Multifactor, IShares Equity, SPDR MSCI, and IShares MSCI. The fund normally invests at least 80 percent of assets in securities included in the Fidelity U.S More
Fidelity Value 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 Value'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 Value Factor upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.8132 | |||
| Information Ratio | (0.02) | |||
| Maximum Drawdown | 2.88 | |||
| Value At Risk | (1.17) | |||
| Potential Upside | 1.0 |
Fidelity Value Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Fidelity Value's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Fidelity Value's standard deviation. In reality, there are many statistical measures that can use Fidelity Value historical prices to predict the future Fidelity Value's volatility.| Risk Adjusted Performance | 0.0761 | |||
| Jensen Alpha | (0.01) | |||
| Total Risk Alpha | (0.02) | |||
| Sortino Ratio | (0.02) | |||
| Treynor Ratio | 0.0728 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Fidelity Value'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 Value Factor Backtested Returns
We consider Fidelity Value very steady. Fidelity Value Factor secures Sharpe Ratio (or Efficiency) of 0.0987, which denotes the etf had a 0.0987% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for Fidelity Value Factor, which you can use to evaluate the volatility of the entity. Please confirm Fidelity Value's Coefficient Of Variation of 828.04, mean deviation of 0.5165, and Downside Deviation of 0.8132 to check if the risk estimate we provide is consistent with the expected return of 0.0683%. The etf shows a Beta (market volatility) of 0.98, which means possible diversification benefits within a given portfolio. Fidelity Value returns are very sensitive to returns on the market. As the market goes up or down, Fidelity Value is expected to follow.
Auto-correlation | 0.62 |
Good predictability
Fidelity Value Factor has good predictability. Overlapping area represents the amount of predictability between Fidelity Value time series from 1st of May 2023 to 28th of October 2023 and 28th of October 2023 to 25th 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 Fidelity Value Factor price movement. The serial correlation of 0.62 indicates that roughly 62.0% of current Fidelity Value price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.62 | |
| Spearman Rank Test | 0.39 | |
| Residual Average | 0.0 | |
| Price Variance | 7.21 |
Fidelity Value Factor lagged returns against current returns
Autocorrelation, which is Fidelity Value 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 Value's etf expected returns. We can calculate the autocorrelation of Fidelity Value returns to help us make a trade decision. For example, suppose you find that Fidelity Value 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 Value 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 Value etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Fidelity Value etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Fidelity Value etf over time.
Current vs Lagged Prices |
| Timeline |
Fidelity Value Lagged Returns
When evaluating Fidelity Value's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Fidelity Value etf have on its future price. Fidelity Value 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 Value autocorrelation shows the relationship between Fidelity Value etf current value and its past values and can show if there is a momentum factor associated with investing in Fidelity Value Factor.
Regressed Prices |
| Timeline |
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Try AI Portfolio ArchitectCheck out Fidelity Value Correlation, Fidelity Value Volatility and Fidelity Value Alpha and Beta module to complement your research on Fidelity Value. Note that the Fidelity Value Factor information on this page should be used as a complementary analysis to other Fidelity Value's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Equity Valuation module to check real value of public entities based on technical and fundamental data.
Fidelity Value 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.