SCIENCE TECHNOLOGY Mutual Fund Forward View - Polynomial Regression
| USSCX Fund | USD 29.18 -1.00 -3.31% |
Science Technology Fund's Polynomial Regression reference page covers the model's projected value and error measures from recent price data. The forecast output and associated deviation metrics are shown for informational use. The model is fitted to available historical daily prices for SCIENCE TECHNOLOGY. This page is updated as new daily closing prices become available for SCIENCE TECHNOLOGY.
The Polynomial Regression forecasted value of Science Technology Fund on the next trading day is expected to be 30.21 with a mean absolute deviation of 0.43 and the sum of the absolute errors of 26.45.A single variable polynomial regression model attempts to put a curve through the SCIENCE TECHNOLOGY 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 All Polynomial Regression forecast figures shown for Science Technology Fund are reference data reflecting model output based on available historical prices. Polynomial Regression Price Forecast For the 27th of March
Given 90 days horizon, the Polynomial Regression forecasted value of Science Technology Fund on the next trading day is expected to be 30.21 with a mean absolute deviation of 0.43 , mean absolute percentage error of 0.31 , and the sum of the absolute errors of 26.45 .Please note that although there have been many attempts to predict SCIENCE 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 SCIENCE TECHNOLOGY's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Mutual Fund Forecast Pattern
| Backtest SCIENCE TECHNOLOGY | SCIENCE TECHNOLOGY Price Prediction | Research Analysis |
Forecasted Value
Forecasting Science Technology Fund for the next session involves measuring the model's historical ability to define credible downside and upside scenarios. 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 SCIENCE TECHNOLOGY mutual fund data series using in forecasting. Note that when a statistical model is used to represent SCIENCE TECHNOLOGY 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 | 116.942 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.4336 |
| MAPE | Mean absolute percentage error | 0.0138 |
| SAE | Sum of the absolute errors | 26.4508 |
Other Forecasting Options for SCIENCE TECHNOLOGY
Bollinger Bands applied to SCIENCE Mutual Fund price data measure how far SCIENCE has deviated from its recent average relative to its own volatility. This distinction drives the choice of forecasting model applied to SCIENCE TECHNOLOGY's price data. On-balance volume for SCIENCE Mutual Fund creates a running indicator of buying versus selling pressure in SCIENCE. Price departures from the channel boundary often mean-revert, offering tactical signals for SCIENCE TECHNOLOGY's.SCIENCE TECHNOLOGY Related Equities
These firms work in a similar space as SCIENCE TECHNOLOGY within the Technology space and serve as useful points for comparison. Return on equity across these peers shows how well each firm turns capital into profit. Peer pricing works best when the firms compared share similar business models and end markets. The peer review below gives a clear framework for judging SCIENCE TECHNOLOGY's standing among rivals.
| Risk & Return | Correlation |
SCIENCE TECHNOLOGY Market Strength Events
For investors tracking Science Technology Fund, market strength indicators offer quantitative evaluation of mutual fund behavior. These indicators add context to timing decisions around Science Technology Fund positions. These indicators capture shifts in momentum that may precede significant price moves in SCIENCE TECHNOLOGY. These metrics provide actionable context for both entry and risk management decisions around Science Technology Fund.
| Rate Of Daily Change | 0.97 | |||
| Day Median Price | 29.18 | |||
| Day Typical Price | 29.18 | |||
| Price Action Indicator | -0.50 | |||
| Period Momentum Indicator | -1.00 | |||
| Relative Strength Index | 38.69 |
SCIENCE TECHNOLOGY Risk Indicators
Analyzing SCIENCE TECHNOLOGY's basic risk indicators provides investors with a structured view of the risk-return trade-off for science mutual fund. By identifying the level of risk embedded in SCIENCE TECHNOLOGY's investment, investors can make informed decisions about position sizing. Analyzing SCIENCE TECHNOLOGY's risk indicators gives investors important context for price forecasting. Understanding the risk in SCIENCE TECHNOLOGY's investment allows investors to make informed choices about mitigating exposure.
| Mean Deviation | 1.08 | |||
| Standard Deviation | 1.37 | |||
| Variance | 1.89 |
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 SCIENCE TECHNOLOGY
The amount of media and story coverage tied to Science Technology Fund can signal where market attention is concentrating at the moment. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.
Other Macroaxis Stories
Macroaxis story coverage is designed for a broad investing audience that ranges from self-directed traders to advisers, researchers, and institutional market participants. The content is intended to support people who want a more structured path from headline information to portfolio action.