FT Cboe ETF Forward View - Polynomial Regression

FJUN ETF

USD 59.39 0.07 0.12%

FT Cboe Vest's Polynomial Regression forecast is generated from the selected price series and evaluated against observed values. Forecast accuracy depends on how stable the recent price trend has been — trending markets suit some models better than others. The forecast is recalculated with each session so it does not rely on stale inputs. A small Bias confirms the model is not systematically over- or under-predicting. The Polynomial Regression model projects FT Cboe at 59.96 for the next trading day, above the most recent closing price. All values shown are model-generated projections and should be evaluated alongside other analytical inputs.

Polynomial regression for FT Cboe fits a curved line through historical price points using time as the independent variable. Unlike simple regression, which fits only a straight line, polynomial regression can capture nonlinear price trends including acceleration and deceleration.

Polynomial Regression Price Forecast For the 11th of May 2026

Over a 90-day horizon, the Polynomial Regression model forecasts FT Cboe at 59.96 for the next trading day, with a mean absolute deviation of 0.40 , mean absolute percentage error of 0.01 , and sum of absolute errors of 24.22 .

This represents a very tight forecast — the model closely tracks FT Cboe's recent price behavior. This output is intended for short-term analytical reference.

ETF Forecast Pattern

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FT Cboe Price Prediction

Research Analysis

Forecasted Value

The projected range for FT Cboe reflects the model's ability to define credible downside and upside scenarios for the next trading day. The model places downside around 59.47 and upside around 60.45 for the next session. The narrow range indicates limited short-term dispersion.

Market Value

59.39

59.47

Downside

59.96

Expected Value

60.45

Upside

Target Odds

Above Odds

Below Odds

Model Predictive Factors

The table below summarizes the Polynomial Regression model's error metrics for FT Cboe ETF. Lower MAD and MAPE values indicate tighter forecast accuracy. AIC measures relative model quality — lower values indicate less information loss and a better-fitting model. A large Bias suggests systematic over- or under-prediction.

AIC	Akaike Information Criteria	116.7192
Bias	Arithmetic mean of the errors	None
MAD	Mean absolute deviation	0.397
MAPE	Mean absolute percentage error	0.0069
SAE	Sum of the absolute errors	24.2163

The model takes the form: Y = a₀ + a₁*X + a₂*X² + a₃*X³ + ... + a_m*X^m. Higher-degree polynomials fit FT Cboe Vest historical data more closely but are more prone to overfitting, which can produce unreliable extrapolations beyond the observed price range.

Other Forecasting Options for FT Cboe

MACD analysis of FJUN tracks the relationship between two exponential moving averages of FT Cboe's price. Many FT Cboe's traders use Fibonacci levels to set entry and exit targets based on prior price swings. Average True Range measures the typical daily price swing for FJUN, accounting for gaps. The frequency and magnitude of gaps reveal how much new information is being priced into FJUN outside regular hours.

FT Cboe Comparable Funds

The instruments listed below are comparable funds for FT Cboe and provide a practical reference set. This peer set gives investors a way to compare FT Cboe's structure and outcomes against similar portfolio vehicles. Category-relative analysis helps separate fund-specific behavior from broader market moves affecting the whole group.

First Trust Exchange Traded FT Cboe Vest First Trust Exchange Traded FT Cboe Vest FT Cboe Vest First Trust Exchange Traded First Trust Exchange Traded FT Cboe Vest FT Cboe Vest FT Cboe Vest American Airlines Group Alcoa Corp Apple Inc Best Buy Co Citigroup

Risk & Return

Correlation

FT Cboe Market Strength Events

Market strength indicators for FT Cboe quantify how the ETF responds to shifts in volume and sentiment. These indicators capture shifts in momentum that may precede significant price moves in FT Cboe. The Market Facilitation Index measures how efficiently price moves relative to volume — rising MFI with rising volume signals strong trend participation. Monitoring these indicators for FT Cboe through complete market cycles reveals recurring patterns.

	Accumulation Distribution	0.0022
	Daily Balance Of Power	0.5385
	Rate Of Daily Change	1.0
	Day Median Price	59.41
	Day Typical Price	59.4
	Market Facilitation Index	0.13
	Price Action Indicator	0.02
	Period Momentum Indicator	0.07

FT Cboe Risk Indicators

Analyzing FT Cboe's risk indicators separates symmetric price swings from asymmetric downside exposure. Understanding and quantifying the risks present in FT Cboe helps place recent price behavior in context. These metrics are most informative when compared against similar equities with comparable growth profiles and market capitalization. When semi-deviation is high relative to standard deviation, FT Cboe's losses have been disproportionately large compared to gains.

	Mean Deviation	0.3611
	Semi Deviation	0.3502
	Standard Deviation	0.5068
	Variance	0.2568
	Downside Variance	0.2021
	Semi Variance	0.1226
	Expected Short fall	-0.40

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.

More Resources for FJUN ETF Analysis

Understanding FT Cboe Vest starts with its holdings data, performance history, and fund characteristics. The reports below outline key context for FT Cboe Vest ETF:

FJUN Risk and Volatility Profile

	Naive Prediction
	Simple Exponential Smoothing
	Double Exponential Smoothing
	Triple Exponential Smoothing
	Simple Regression
	Polynomial Regression
	Simple Moving Average
	4 Period Moving Average
	8 Period Moving Average
	20 Period Moving Average