Cboe Vest Mutual Fund Forecast - Simple Regression

BUCGX Fund  USD 20.93  0.06  0.29%   
The Simple Regression forecasted value of Cboe Vest Large on the next trading day is expected to be 21.00 with a mean absolute deviation of 0.12 and the sum of the absolute errors of 7.33. Cboe Mutual Fund Forecast is based on your current time horizon.
At this time, The relative strength index (RSI) of Cboe Vest's share price is at 55 suggesting that the mutual fund is in nutural position, most likellhy at or near its resistance level. The main idea of RSI analysis is to track how fast people are buying or selling Cboe Vest, making its price go up or down.

Momentum 55

 Impartial

 
Oversold
 
Overbought
The successful prediction of Cboe Vest's future price could yield a significant profit. We analyze noise-free headlines and recent hype associated with Cboe Vest Large, which may create opportunities for some arbitrage if properly timed.
Using Cboe Vest hype-based prediction, you can estimate the value of Cboe Vest Large from the perspective of Cboe Vest response to recently generated media hype and the effects of current headlines on its competitors.
The Simple Regression forecasted value of Cboe Vest Large on the next trading day is expected to be 21.00 with a mean absolute deviation of 0.12 and the sum of the absolute errors of 7.33.

Cboe Vest after-hype prediction price

    
  USD 20.93  
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 fund price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
  
Check out Historical Fundamental Analysis of Cboe Vest to cross-verify your projections.

Cboe Vest Additional Predictive Modules

Most predictive techniques to examine Cboe price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for Cboe using various technical indicators. When you analyze Cboe 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.
Simple Regression model is a single variable regression model that attempts to put a straight line through Cboe Vest price points. This line is defined by its gradient or slope, and the point at which it intercepts the x-axis. Mathematically, assuming the independent variable is X and the dependent variable is Y, then this line can be represented as: Y = intercept + slope * X.

Cboe Vest Simple Regression Price Forecast For the 24th of January

Given 90 days horizon, the Simple Regression forecasted value of Cboe Vest Large on the next trading day is expected to be 21.00 with a mean absolute deviation of 0.12, mean absolute percentage error of 0.03, and the sum of the absolute errors of 7.33.
Please note that although there have been many attempts to predict Cboe Mutual Fund 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 Cboe Vest's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Cboe Vest Mutual Fund Forecast Pattern

Backtest Cboe VestCboe Vest Price PredictionBuy or Sell Advice 

Cboe Vest Forecasted Value

In the context of forecasting Cboe Vest's Mutual Fund 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. Cboe Vest's downside and upside margins for the forecasting period are 20.12 and 21.87, respectively. We have considered Cboe Vest'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.
Market Value
20.93
21.00
Expected Value
21.87
Upside

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 Cboe Vest mutual fund data series using in forecasting. Note that when a statistical model is used to represent Cboe Vest mutual fund, 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.
AICAkaike Information Criteria116.4011
BiasArithmetic mean of the errors None
MADMean absolute deviation0.1182
MAPEMean absolute percentage error0.0058
SAESum of the absolute errors7.3308
In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as Cboe Vest Large historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Predictive Modules for Cboe Vest

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Cboe Vest Large. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual fund 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.
Hype
Prediction
LowEstimatedHigh
20.0620.9321.80
Details
Intrinsic
Valuation
LowRealHigh
19.9320.8021.67
Details
Bollinger
Band Projection (param)
LowMiddleHigh
20.6720.8721.07
Details
Please note, it is not enough to conduct a financial or market analysis of a single entity such as Cboe Vest. Your research has to be compared to or analyzed against Cboe Vest's peers to derive any actionable benefits. When done correctly, Cboe Vest's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in Cboe Vest Large.

Cboe Vest After-Hype Price Prediction Density Analysis

As far as predicting the price of Cboe Vest 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 Cboe Vest 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 Mutual Fund prices, such as prices of Cboe Vest, with the unreliable approximations that try to describe financial returns.
   Next price density   
       Expected price to next headline  

Cboe Vest Estimiated After-Hype Price Volatility

In the context of predicting Cboe Vest's mutual fund value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on Cboe Vest's historical news coverage. Cboe Vest's after-hype downside and upside margins for the prediction period are 20.06 and 21.80, respectively. We have considered Cboe Vest'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
20.93
20.93
After-hype Price
21.80
Upside
Cboe Vest is very steady at this time. Analysis and calculation of next after-hype price of Cboe Vest Large is based on 3 months time horizon.

Cboe Vest Mutual Fund Price Prediction Analysis

Have you ever been surprised when a price of a Mutual Fund such as Cboe Vest is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading Cboe Vest 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 Fund 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 Cboe Vest, there might be something going there, and it might present an excellent short sale opportunity.
Expected ReturnPeriod VolatilityHype ElasticityRelated ElasticityNews DensityRelated DensityExpected Hype
  0.04 
0.88
 0.00  
  0.06 
0 Events / Month
0 Events / Month
In 5 to 10 days
Latest traded priceExpected after-news pricePotential return on next major newsAverage after-hype volatility
20.93
20.93
0.00 
0.00  
Notes

Cboe Vest Hype Timeline

Cboe Vest Large is currently traded for 20.93. The entity stock is not elastic to its hype. The average elasticity to hype of competition is 0.06. Cboe 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 insignificant. The immediate return on the next news is forecasted to be very small, whereas the daily expected return is currently at 0.04%. %. The volatility of related hype on Cboe Vest is about 63.37%, with the expected price after the next announcement by competition of 20.99. The company last dividend was issued on the 31st of December 2019. Assuming the 90 days horizon the next forecasted press release will be in 5 to 10 days.
Check out Historical Fundamental Analysis of Cboe Vest to cross-verify your projections.

Cboe Vest Related Hype Analysis

Having access to credible news sources related to Cboe Vest's direct competition is more important than ever and may enhance your ability to predict Cboe Vest's future price movements. Getting to know how Cboe Vest'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 Cboe Vest may potentially react to the hype associated with one of its peers.

Other Forecasting Options for Cboe Vest

For every potential investor in Cboe, whether a beginner or expert, Cboe Vest's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Cboe Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Cboe. Basic forecasting techniques help filter out the noise by identifying Cboe Vest's price trends.

Cboe Vest 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 Cboe Vest mutual fund to make a market-neutral strategy. Peer analysis of Cboe Vest could also be used in its relative valuation, which is a method of valuing Cboe Vest by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

Cboe Vest Market Strength Events

Market strength indicators help investors to evaluate how Cboe Vest mutual fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Cboe Vest shares will generate the highest return on investment. By undertsting and applying Cboe Vest mutual fund market strength indicators, traders can identify Cboe Vest Large entry and exit signals to maximize returns.

Cboe Vest Risk Indicators

The analysis of Cboe Vest'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 Cboe Vest's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting cboe mutual fund 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.
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 Cboe Vest

The number of cover stories for Cboe Vest depends on current market conditions and Cboe Vest's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that Cboe Vest 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 Cboe Vest'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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Other Information on Investing in Cboe Mutual Fund

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