FT Cboe Etf Forecast - Simple Regression
| FMAR Etf | USD 47.73 0.03 0.06% |
FMAR Etf outlook is based on your current time horizon.
The relative strength momentum indicator of FT Cboe's etf price is slightly above 61. This usually indicates that the etf is rather overbought by investors as of today. The main point of the Relative Strength Index (RSI) is to track how fast people are buying or selling FMAR, making its price go up or down. Momentum 61
Buy Extended
Oversold | Overbought |
Using FT Cboe hype-based prediction, you can estimate the value of FT Cboe Vest from the perspective of FT Cboe response to recently generated media hype and the effects of current headlines on its competitors.
The Simple Regression forecasted value of FT Cboe Vest on the next trading day is expected to be 47.82 with a mean absolute deviation of 0.12 and the sum of the absolute errors of 7.27. FT Cboe after-hype prediction price | USD 47.73 |
There is no one specific way to measure market sentiment using hype analysis or a similar predictive technique. This prediction method should be used in combination with more fundamental and traditional techniques such as etf price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
Check out Historical Fundamental Analysis of FT Cboe to cross-verify your projections. FT Cboe Additional Predictive Modules
Most predictive techniques to examine FMAR price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for FMAR using various technical indicators. When you analyze FMAR charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
FT Cboe Simple Regression Price Forecast For the 27th of January
Given 90 days horizon, the Simple Regression forecasted value of FT Cboe Vest on the next trading day is expected to be 47.82 with a mean absolute deviation of 0.12, mean absolute percentage error of 0.03, and the sum of the absolute errors of 7.27.Please note that although there have been many attempts to predict FMAR Etf prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that FT Cboe's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
FT Cboe Etf Forecast Pattern
| Backtest FT Cboe | FT Cboe Price Prediction | Buy or Sell Advice |
FT Cboe Forecasted Value
In the context of forecasting FT Cboe's Etf value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. FT Cboe's downside and upside margins for the forecasting period are 47.60 and 48.04, respectively. We have considered FT Cboe's daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of FT Cboe etf data series using in forecasting. Note that when a statistical model is used to represent FT Cboe etf, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.| AIC | Akaike Information Criteria | 114.5879 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.1193 |
| MAPE | Mean absolute percentage error | 0.0025 |
| SAE | Sum of the absolute errors | 7.2749 |
Predictive Modules for FT Cboe
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as FT Cboe Vest. Regardless of method or technology, however, to accurately forecast the etf market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the etf market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.FT Cboe After-Hype Price Density Analysis
As far as predicting the price of FT Cboe at your current risk attitude, this probability distribution graph shows the chance that the prediction will fall between or within a specific range. We use this chart to confirm that your returns on investing in FT Cboe or, for that matter, your successful expectations of its future price, cannot be replicated consistently. Please note, a large amount of money has been lost over the years by many investors who confused the symmetrical distributions of Etf prices, such as prices of FT Cboe, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
FT Cboe Estimiated After-Hype Price Volatility
In the context of predicting FT Cboe's etf value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on FT Cboe's historical news coverage. FT Cboe's after-hype downside and upside margins for the prediction period are 47.51 and 47.95, respectively. We have considered FT Cboe's daily market price in relation to the headlines to evaluate this method's predictive performance. Remember, however, there is no scientific proof or empirical evidence that news-based prediction models outperform traditional linear, nonlinear models or artificial intelligence models to provide accurate predictions consistently.
Current Value
FT Cboe is very steady at this time. Analysis and calculation of next after-hype price of FT Cboe Vest is based on 3 months time horizon.
FT Cboe Etf Price Outlook Analysis
Have you ever been surprised when a price of a ETF such as FT Cboe is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading FT Cboe backward and forwards among themselves. Have you ever observed a lot of a particular company's price movement is driven by press releases or news about the company that has nothing to do with actual earnings? Usually, hype to individual companies acts as price momentum. If not enough favorable publicity is forthcoming, the Etf price eventually runs out of speed. So, the rule of thumb here is that as long as this news hype has nothing to do with immediate earnings, you should pay more attention to it. If you see this tendency with FT Cboe, there might be something going there, and it might present an excellent short sale opportunity.
| Expected Return | Period Volatility | Hype Elasticity | Related Elasticity | News Density | Related Density | Expected Hype |
0.03 | 0.22 | 0.00 | 0.00 | 5 Events / Month | 4 Events / Month | In about 5 days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
47.73 | 47.73 | 0.00 |
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FT Cboe Hype Timeline
FT Cboe Vest is currently traded for 47.73. The entity stock is not elastic to its hype. The average elasticity to hype of competition is 0.0. FMAR is forecasted not to react to the next headline, with the price staying at about the same level, and average media hype impact volatility is over 100%. The immediate return on the next news is forecasted to be very small, whereas the daily expected return is currently at 0.03%. %. The volatility of related hype on FT Cboe is about 3142.86%, with the expected price after the next announcement by competition of 47.73. About 13.0% of the company shares are held by company insiders. The company recorded a loss per share of 0.55. FT Cboe Vest last dividend was issued on the February 10, 2000. The entity had 11:10 split on the May 21, 1998. Given the investment horizon of 90 days the next forecasted press release will be in about 5 days. Check out Historical Fundamental Analysis of FT Cboe to cross-verify your projections.FT Cboe Related Hype Analysis
Having access to credible news sources related to FT Cboe's direct competition is more important than ever and may enhance your ability to predict FT Cboe's future price movements. Getting to know how FT Cboe's peers react to changing market sentiment, related social signals, and mainstream news is a great way to find investing opportunities and time the market. The summary table below summarizes the essential lagging indicators that can help you analyze how FT Cboe may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| FAPR | FT Cboe Vest | (0.05) | 3 per month | 0.00 | (0.25) | 0.35 | (0.25) | 0.93 | |
| FOCT | First Trust Exchange Traded | 0.06 | 3 per month | 0.40 | (0.1) | 0.72 | (0.64) | 2.41 | |
| FNOV | FT Cboe Vest | (0.03) | 2 per month | 0.36 | (0.06) | 0.71 | (0.54) | 2.29 | |
| FMAY | First Trust Exchange Traded | (0.03) | 10 per month | 0.21 | (0.13) | 0.48 | (0.49) | 1.70 | |
| FJUN | FT Cboe Vest | (0.02) | 7 per month | 0.21 | (0.15) | 0.46 | (0.53) | 1.59 | |
| FSEP | FT Cboe Vest | (0.03) | 15 per month | 0.43 | (0.1) | 0.63 | (0.74) | 2.44 | |
| FDEC | First Trust Exchange Traded | (0.03) | 4 per month | 0.34 | (0.04) | 0.77 | (0.77) | 2.32 | |
| PAPR | Innovator SP 500 | 0.07 | 1 per month | 0.00 | (0.30) | 0.31 | (0.26) | 0.80 | |
| FAUG | FT Cboe Vest | (0.03) | 6 per month | 0.43 | (0.10) | 0.63 | (0.63) | 2.32 | |
| FJAN | First Trust Exchange Traded | 0.02 | 5 per month | 0.34 | (0.07) | 0.64 | (0.78) | 2.43 |
Other Forecasting Options for FT Cboe
For every potential investor in FMAR, whether a beginner or expert, FT Cboe's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. FMAR Etf price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in FMAR. Basic forecasting techniques help filter out the noise by identifying FT Cboe's price trends.FT Cboe Related Equities
One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with FT Cboe etf to make a market-neutral strategy. Peer analysis of FT Cboe could also be used in its relative valuation, which is a method of valuing FT Cboe by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
FT Cboe Market Strength Events
Market strength indicators help investors to evaluate how FT Cboe etf reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading FT Cboe shares will generate the highest return on investment. By undertsting and applying FT Cboe etf market strength indicators, traders can identify FT Cboe Vest entry and exit signals to maximize returns.
FT Cboe Risk Indicators
The analysis of FT Cboe's basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in FT Cboe's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting fmar etf prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
| Mean Deviation | 0.1589 | |||
| Semi Deviation | 0.0783 | |||
| Standard Deviation | 0.2176 | |||
| Variance | 0.0474 | |||
| Downside Variance | 0.0526 | |||
| Semi Variance | 0.0061 | |||
| Expected Short fall | (0.17) |
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.
Story Coverage note for FT Cboe
The number of cover stories for FT Cboe depends on current market conditions and FT Cboe's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that FT Cboe is classified under. However, while its typical story may have numerous social followers, the rapid visibility can also attract short-sellers, who usually are skeptical about FT Cboe's long-term prospects. So, having above-average coverage will typically attract above-average short interest, leading to significant price volatility.
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Check out Historical Fundamental Analysis of FT Cboe to cross-verify your projections. You can also try the Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
The market value of FT Cboe Vest is measured differently than its book value, which is the value of FMAR 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.