IPath Series ETF Forward View - 20 Period Moving Average
| VXZ ETF | USD 55.95 0.46 0.83% |
20 Period Moving Average is applied to iPath Series B's daily closing prices, and the resulting forecast is presented with accuracy metrics. Wide deviation between fitted and observed values suggests the model's assumptions may not match current market conditions. The 20 Period Moving Average model projects IPath Series at 55.53 for the next trading day, below the most recent closing price. This 20 Period Moving Average output is provided as analytical reference and does not constitute a trading recommendation.
20 Period Moving Average Price Forecast For the 12th of May 2026
Over a 90-day horizon, the 20 Period Moving Average model forecasts IPath Series at 55.53 for the next trading day, with a mean absolute deviation of 1.81 , mean absolute percentage error of 0.03 , and sum of absolute errors of 74.01 .This represents a tight forecast with good short-term tracking of IPath Series' price movement. This output is intended for short-term analytical reference.
ETF Forecast Pattern
| Backtest IPath Series | IPath Series Price Prediction | Research Analysis |
Forecasted Value
The next-day forecast range for IPath Series defines statistically derived downside and upside boundaries based on model performance. The forecast band spans 54.10 to 56.96. The moderate spread reflects defined uncertainty around the forecast.
Model Predictive Factors
The table below summarizes the 20 Period Moving Average model's error metrics for IPath Series 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 | 82.8787 |
| Bias | Arithmetic mean of the errors | 0.2772 |
| MAD | Mean absolute deviation | 1.8052 |
| MAPE | Mean absolute percentage error | 0.0315 |
| SAE | Sum of the absolute errors | 74.0125 |
Other Forecasting Options for IPath Series
IPath Series' daily price returns decompose into trend, seasonal, and residual components. Divergence between short-term and long-term averages in IPath Series often signals an upcoming reversal or acceleration.IPath Series Comparable Funds
Investors studying IPath Series often review similar funds to compare yield, discount behavior, and risk. Looking across similar funds helps show whether IPath Series' pricing and risk profile are typical for the category. Category-relative analysis helps separate fund-specific behavior from broader market moves affecting the whole group. Taken together, these peers help define a more relevant comparison frame for IPath Series.
| Risk & Return | Correlation |
IPath Series Market Strength Events
Market strength indicators for IPath Series ETF provide a framework for assessing security responsiveness. A rising Accumulation/Distribution line alongside rising price confirms institutional buying interest in IPath Series.
IPath Series Risk Indicators
Assessing IPath Series' risk indicators is a structured way to evaluate the risk-return trade-off for ipath series etf. The level of risk embedded in IPath Series' feeds directly into exposure calibration.
| Mean Deviation | 1.15 | |||
| Semi Deviation | 1.43 | |||
| Standard Deviation | 1.46 | |||
| Variance | 2.14 | |||
| Downside Variance | 2.18 | |||
| Semi Variance | 2.05 | |||
| Expected Short fall | -1.16 |
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 IPath Series ETF Analysis
iPath Series B can be assessed through both market price and NAV, which can tell different stories during volatile periods.
IPath Series market price and NAV can differ because they are formed through different mechanisms. Assessment often reviews fund costs, underlying exposure, category peers, and benchmark tracking precision.