Ft Cboe Vest Etf Market Value
| GAUG Etf | 35.50 0.06 0.17% |
| Symbol | GAUG |
The market value of FT Cboe Vest is measured differently than its book value, which is the value of GAUG 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.
| 10/31/2024 |
| 11/30/2024 |
If you would invest 0.00 in FT Cboe on October 31, 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 FT Vest, Northern Lights, Dimensional International, Matthews China, Davis Select, First Trust, and EA Series. FT Cboe is entity of United States. It is traded as Etf on BATS exchange. 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 | 0.4189 | |||
| Information Ratio | (0.21) | |||
| Maximum Drawdown | 1.69 | |||
| Value At Risk | (0.55) | |||
| Potential Upside | 0.5645 |
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 | 0.1123 | |||
| Jensen Alpha | (0.0004) | |||
| Total Risk Alpha | (0.01) | |||
| Sortino Ratio | (0.19) | |||
| Treynor Ratio | 0.1267 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of FT Cboe'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.
FT Cboe Vest Backtested Returns
At this point, FT Cboe is very steady. FT Cboe Vest retains Efficiency (Sharpe Ratio) of 0.22, which denotes the etf had a 0.22% 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 Standard Deviation of 0.3659, market risk adjusted performance of 0.1367, and Downside Deviation of 0.4189 to check if the risk estimate we provide is consistent with the expected return of 0.0712%. 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 | 0.92 |
Excellent predictability
FT Cboe Vest has excellent predictability. Overlapping area represents the amount of predictability between FT Cboe time series from 31st of October 2024 to 15th of November 2024 and 15th of November 2024 to 30th 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 0.92 indicates that approximately 92.0% of current FT Cboe price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.92 | |
| Spearman Rank Test | 0.85 | |
| Residual Average | 0.0 | |
| Price Variance | 0.02 |
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 |
Currently Active Assets on Macroaxis
When determining whether FT Cboe Vest is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if GAUG Etf is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about Ft Cboe Vest Etf. Highlighted below are key reports to facilitate an investment decision about Ft Cboe Vest Etf: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 Stock Tickers module to use high-impact, comprehensive, and customizable stock tickers that can be easily integrated to any websites.
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.