VANGUARD SHORT-TERM Mutual Fund Forward View - Polynomial Regression
| VFSIX Fund | USD 10.43 -0.02 -0.19% |
The Polynomial Regression forecast shown here for VANGUARD SHORT-TERM is reference data produced from the equity's historical price series. Accuracy metrics including mean absolute deviation are provided alongside the projection.
The Polynomial Regression forecasted value of Vanguard Short Term Investment Grade on the next trading day is expected to be 10.42 with a mean absolute deviation of 0.01 and the sum of the absolute errors of 0.88.A single variable polynomial regression model attempts to put a curve through the VANGUARD SHORT-TERM historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm This Polynomial Regression reference page for VANGUARD SHORT-TERM presents model-generated projections from historical price data for informational purposes. Polynomial Regression Price Forecast For the 20th of March
Given 90 days horizon, the Polynomial Regression forecasted value of Vanguard Short Term Investment Grade on the next trading day is expected to be 10.42 with a mean absolute deviation of 0.01 , mean absolute percentage error of 0.0003 , and the sum of the absolute errors of 0.88 .Please note that although there have been many attempts to predict VANGUARD Mutual Fund prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that VANGUARD SHORT-TERM's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Mutual Fund Forecast Pattern
| Backtest VANGUARD SHORT-TERM | VANGUARD SHORT-TERM Price Prediction | Research Analysis |
Forecasted Value
For the next trading day, Macroaxis evaluates VANGUARD SHORT-TERM's predictive range by looking for statistically meaningful downside and upside boundaries. Used properly, these levels provide context around forecast dispersion rather than certainty about the next closing print.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of VANGUARD SHORT-TERM mutual fund data series using in forecasting. Note that when a statistical model is used to represent VANGUARD SHORT-TERM 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.| AIC | Akaike Information Criteria | 111.903 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0141 |
| MAPE | Mean absolute percentage error | 0.0013 |
| SAE | Sum of the absolute errors | 0.875 |
Other Forecasting Options for VANGUARD SHORT-TERM
Regardless of investment experience, understanding VANGUARD SHORT-TERM's price movement is essential for anyone considering a position in VANGUARD. Price charts for VANGUARD Mutual Fund are often filled with noise that can lead to poor investment choices if not properly filtered.VANGUARD SHORT-TERM Related Equities
The following equities are related to VANGUARD SHORT-TERM within the Short-Term Bond space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing VANGUARD SHORT-TERM against peers on metrics such as P/E, margins, and return on equity helps contextualize its positioning and identify relative strengths or weaknesses.
| Risk & Return | Correlation |
VANGUARD SHORT-TERM Market Strength Events
Market strength indicators for VANGUARD SHORT-TERM give investors insight into the mutual fund's responsiveness to broader market forces. Tracking these indicators helps investors make informed timing decisions and identify periods where trading VANGUARD SHORT-TERM is likely to be most rewarding.
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 10.43 | |||
| Day Typical Price | 10.43 | |||
| Price Action Indicator | -0.01 | |||
| Period Momentum Indicator | -0.02 | |||
| Relative Strength Index | 42.09 |
VANGUARD SHORT-TERM Risk Indicators
A thorough review of VANGUARD SHORT-TERM's risk indicators is an important first step in forecasting its price and managing investment exposure. This analysis helps investors determine the appropriate level of risk to accept when holding VANGUARD SHORT-TERM's.
| Mean Deviation | 0.0844 | |||
| Semi Deviation | 0.063 | |||
| Standard Deviation | 0.1248 | |||
| Variance | 0.0156 | |||
| Downside Variance | 0.0196 | |||
| Semi Variance | 0.004 | |||
| Expected Short fall | -0.15 |
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 VANGUARD SHORT-TERM
Story coverage around Vanguard Short Term Investment Grade often expands when market conditions, narrative momentum, or risk-adjusted performance make the security more visible to investors. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.
Other Macroaxis Stories
Macroaxis publishes story content for a diverse readership that includes finance students, independent investors, money managers, and market-focused operating teams. What connects that audience is a focus on building stronger portfolios through better research, risk awareness, and comparative analysis.