Fidelity Disruptive Finance Etf Market Value
| FDFF Etf | 34.05 0.80 2.41% |
| Symbol | Fidelity |
Investors evaluate Fidelity Disruptive using market value (trading price) and book value (balance sheet equity), each telling a different story. Calculating Fidelity Disruptive's intrinsic value - the estimated true worth - helps identify when the stock trades at a discount or premium to fair value. Market participants employ diverse analytical approaches to determine fair value and identify buying opportunities when prices dip below calculated worth. External factors like market trends, sector rotation, and investor psychology can cause Fidelity Disruptive's market price to deviate significantly from intrinsic value.
Please note, there is a significant difference between Fidelity Disruptive's value and its price as these two are different measures arrived at by different means. Investors typically determine if Fidelity Disruptive is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. Conversely, Fidelity Disruptive's market price signifies the transaction level at which participants voluntarily complete trades.
Fidelity Disruptive '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 Disruptive'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 Disruptive.
| 11/08/2025 |
| 02/06/2026 |
If you would invest 0.00 in Fidelity Disruptive on November 8, 2025 and sell it all today you would earn a total of 0.00 from holding Fidelity Disruptive Finance or generate 0.0% return on investment in Fidelity Disruptive over 90 days. Fidelity Disruptive is related to or competes with Fidelity Disruptive, Invesco DWA, FundX Investment, American Beacon, Affinity World, Invesco DWA, and FundX Investment. Fidelity Disruptive is entity of United States More
Fidelity Disruptive 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 Disruptive'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 Disruptive Finance upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.10) | |||
| Maximum Drawdown | 6.45 | |||
| Value At Risk | (1.98) | |||
| Potential Upside | 1.58 |
Fidelity Disruptive Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Fidelity Disruptive's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Fidelity Disruptive's standard deviation. In reality, there are many statistical measures that can use Fidelity Disruptive historical prices to predict the future Fidelity Disruptive's volatility.| Risk Adjusted Performance | (0.04) | |||
| Jensen Alpha | (0.12) | |||
| Total Risk Alpha | (0.13) | |||
| Treynor Ratio | (0.07) |
Fidelity Disruptive February 6, 2026 Technical Indicators
| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
| Risk Adjusted Performance | (0.04) | |||
| Market Risk Adjusted Performance | (0.06) | |||
| Mean Deviation | 0.8738 | |||
| Coefficient Of Variation | (1,613) | |||
| Standard Deviation | 1.13 | |||
| Variance | 1.28 | |||
| Information Ratio | (0.10) | |||
| Jensen Alpha | (0.12) | |||
| Total Risk Alpha | (0.13) | |||
| Treynor Ratio | (0.07) | |||
| Maximum Drawdown | 6.45 | |||
| Value At Risk | (1.98) | |||
| Potential Upside | 1.58 | |||
| Skewness | (0.53) | |||
| Kurtosis | 1.0 |
Fidelity Disruptive Backtested Returns
Fidelity Disruptive secures Sharpe Ratio (or Efficiency) of -0.0696, which denotes the etf had a -0.0696 % return per unit of risk over the last 3 months. Fidelity Disruptive Finance exposes twenty-three different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Fidelity Disruptive's Mean Deviation of 0.8738, standard deviation of 1.13, and Variance of 1.28 to check the risk estimate we provide. The etf shows a Beta (market volatility) of 1.18, which means 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, Fidelity Disruptive will likely underperform.
Auto-correlation | -0.65 |
Very good reverse predictability
Fidelity Disruptive Finance has very good reverse predictability. Overlapping area represents the amount of predictability between Fidelity Disruptive time series from 8th of November 2025 to 23rd of December 2025 and 23rd of December 2025 to 6th of February 2026. 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 Disruptive price movement. The serial correlation of -0.65 indicates that roughly 65.0% of current Fidelity Disruptive price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.65 | |
| Spearman Rank Test | -0.34 | |
| Residual Average | 0.0 | |
| Price Variance | 0.83 |
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Check out Fidelity Disruptive Correlation, Fidelity Disruptive Volatility and Fidelity Disruptive Performance module to complement your research on Fidelity Disruptive. You can also try the Price Ceiling Movement module to calculate and plot Price Ceiling Movement for different equity instruments.
Fidelity Disruptive 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.