Ft Cboe Vest Etf Market Value
| DNOV Etf | USD 48.82 0.07 0.14% |
| Symbol | DNOV |
Investors evaluate FT Cboe Vest using market value (trading price) and book value (balance sheet equity), each telling a different story. Calculating FT Cboe's intrinsic value - the estimated true worth - helps identify when the stock trades at a discount or premium to fair value. Investment professionals apply varied valuation frameworks to compute inherent worth and acquire positions when market prices trade at discounts to calculated value. External factors like market trends, sector rotation, and investor psychology can cause FT Cboe's market price to deviate significantly from intrinsic value.
It's important to distinguish between FT Cboe's intrinsic value and market price, which are calculated using different methodologies. Investment decisions regarding FT Cboe should consider multiple factors including financial performance, growth metrics, competitive position, and professional analysis. Conversely, FT Cboe's market price signifies the transaction level at which participants voluntarily complete trades.
FT Cboe '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 FT Cboe'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 FT Cboe.
| 11/16/2025 |
| 02/14/2026 |
If you would invest 0.00 in FT Cboe on November 16, 2025 and sell it all today you would earn a total of 0.00 from holding FT Cboe Vest or generate 0.0% return on investment in FT Cboe over 90 days. FT Cboe is related to or competes with First Trust, First Trust, First Trust, FT Cboe, First Trust, FT Cboe, and First Trust. Under normal market conditions, the fund will invest substantially all of its assets in FLexible EXchange Options that r... More
FT Cboe 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 FT Cboe'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 FT Cboe Vest upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.3747 | |||
| Information Ratio | (0.08) | |||
| Maximum Drawdown | 2.46 | |||
| Value At Risk | (0.58) | |||
| Potential Upside | 0.4806 |
FT Cboe Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for FT Cboe's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as FT Cboe's standard deviation. In reality, there are many statistical measures that can use FT Cboe historical prices to predict the future FT Cboe's volatility.| Risk Adjusted Performance | 0.0779 | |||
| Jensen Alpha | 0.0107 | |||
| Total Risk Alpha | 0.0035 | |||
| Sortino Ratio | (0.08) | |||
| Treynor Ratio | 0.0928 |
FT Cboe February 14, 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 | 0.0779 | |||
| Market Risk Adjusted Performance | 0.1028 | |||
| Mean Deviation | 0.2354 | |||
| Semi Deviation | 0.2592 | |||
| Downside Deviation | 0.3747 | |||
| Coefficient Of Variation | 893.25 | |||
| Standard Deviation | 0.3596 | |||
| Variance | 0.1293 | |||
| Information Ratio | (0.08) | |||
| Jensen Alpha | 0.0107 | |||
| Total Risk Alpha | 0.0035 | |||
| Sortino Ratio | (0.08) | |||
| Treynor Ratio | 0.0928 | |||
| Maximum Drawdown | 2.46 | |||
| Value At Risk | (0.58) | |||
| Potential Upside | 0.4806 | |||
| Downside Variance | 0.1404 | |||
| Semi Variance | 0.0672 | |||
| Expected Short fall | (0.24) | |||
| Skewness | 0.4409 | |||
| Kurtosis | 3.91 |
FT Cboe Vest Backtested Returns
At this stage we consider DNOV Etf to be very steady. FT Cboe Vest retains Efficiency (Sharpe Ratio) of 0.13, which denotes the etf had a 0.13 % return per unit of price deviation over the last 3 months. We have found twenty-eight technical indicators for FT Cboe, which you can use to evaluate the volatility of the entity. Please confirm FT Cboe's Downside Deviation of 0.3747, standard deviation of 0.3596, and Market Risk Adjusted Performance of 0.1028 to check if the risk estimate we provide is consistent with the expected return of 0.0484%. The etf owns a Beta (Systematic Risk) of 0.33, which means possible diversification benefits within a given portfolio. As returns on the market increase, FT Cboe's returns are expected to increase less than the market. However, during the bear market, the loss of holding FT Cboe is expected to be smaller as well.
Auto-correlation | 0.23 |
Weak predictability
FT Cboe Vest has weak predictability. Overlapping area represents the amount of predictability between FT Cboe time series from 16th of November 2025 to 31st of December 2025 and 31st of December 2025 to 14th 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 FT Cboe Vest price movement. The serial correlation of 0.23 indicates that over 23.0% of current FT Cboe price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.23 | |
| Spearman Rank Test | 0.06 | |
| Residual Average | 0.0 | |
| Price Variance | 0.03 |
Thematic Opportunities
Explore Investment Opportunities
Check out FT Cboe Correlation, FT Cboe Volatility and FT Cboe Performance module to complement your research on FT Cboe. You can also try the Stocks Directory module to find actively traded stocks across global markets.
FT Cboe 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.