Ft Cboe Vest Etf Market Value
| FAPR Etf | USD 41.21 0.07 0.17% |
| Symbol | FAPR |
The market value of FT Cboe Vest is measured differently than its book value, which is the value of FAPR that is recorded on the company's balance sheet. Investors also form their own opinion of FT Cboe's value that differs from its market value or its book value, called intrinsic value, which is FT Cboe'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 FT Cboe's market value can be influenced by many factors that don't directly affect FT Cboe'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 FT Cboe's value and its price as these two are different measures arrived at by different means. Investors typically determine if FT Cboe is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, FT Cboe'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.
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.
| 10/24/2024 |
| 11/23/2024 |
If you would invest 0.00 in FT Cboe on October 24, 2024 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 30 days. FT Cboe is related to or competes with First Trust, FT Cboe, Innovator, and FT Cboe. 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 | 0.4348 | |||
| Information Ratio | (0.19) | |||
| Maximum Drawdown | 2.01 | |||
| Value At Risk | (0.46) | |||
| Potential Upside | 0.6653 |
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 | 0.1135 | |||
| Jensen Alpha | 0.0031 | |||
| Total Risk Alpha | (0.01) | |||
| Sortino Ratio | (0.16) | |||
| Treynor Ratio | 0.1286 |
FT Cboe Vest Backtested Returns
Currently, FT Cboe Vest is very steady. FT Cboe Vest retains Efficiency (Sharpe Ratio) of 0.14, which denotes the etf had a 0.14% return per unit of price deviation over the last 3 months. We have found thirty technical indicators for FT Cboe, which you can use to evaluate the volatility of the entity. Please confirm FT Cboe's Downside Deviation of 0.4348, market risk adjusted performance of 0.1386, and Standard Deviation of 0.3759 to check if the risk estimate we provide is consistent with the expected return of 0.0519%. The etf owns a Beta (Systematic Risk) of 0.39, 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 | 0.70 |
Good predictability
FT Cboe Vest has good predictability. Overlapping area represents the amount of predictability between FT Cboe time series from 24th of October 2024 to 8th of November 2024 and 8th of November 2024 to 23rd 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 FT Cboe Vest price movement. The serial correlation of 0.7 indicates that around 70.0% of current FT Cboe price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.7 | |
| Spearman Rank Test | 0.6 | |
| Residual Average | 0.0 | |
| Price Variance | 0.01 |
FT Cboe Vest lagged returns against current returns
Autocorrelation, which is FT Cboe 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 FT Cboe's etf expected returns. We can calculate the autocorrelation of FT Cboe returns to help us make a trade decision. For example, suppose you find that FT Cboe 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 |
FT Cboe 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 FT Cboe etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if FT Cboe etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in FT Cboe etf over time.
Current vs Lagged Prices |
| Timeline |
FT Cboe Lagged Returns
When evaluating FT Cboe's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of FT Cboe etf have on its future price. FT Cboe 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, FT Cboe autocorrelation shows the relationship between FT Cboe etf current value and its past values and can show if there is a momentum factor associated with investing in FT Cboe Vest.
Regressed Prices |
| Timeline |
Pair Trading with FT Cboe
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if FT Cboe position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in FT Cboe will appreciate offsetting losses from the drop in the long position's value.Moving together with FAPR Etf
| 1.0 | BUFR | First Trust Cboe | PairCorr |
| 0.99 | BUFD | FT Cboe Vest | PairCorr |
| 0.99 | PSEP | Innovator SP 500 | PairCorr |
| 0.99 | PJAN | Innovator SP 500 | PairCorr |
The ability to find closely correlated positions to FT Cboe could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace FT Cboe when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back FT Cboe - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling FT Cboe Vest to buy it.
The correlation of FT Cboe is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as FT Cboe moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if FT Cboe Vest moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for FT Cboe can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.Check out FT Cboe Correlation, FT Cboe Volatility and FT Cboe Alpha and Beta module to complement your research on FT Cboe. You can also try the Positions Ratings module to determine portfolio positions ratings based on digital equity recommendations. Macroaxis instant position ratings are based on combination of fundamental analysis and risk-adjusted market performance.
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.