ALGER SPECTRA Mutual Fund Forward View - Polynomial Regression
| ASPIX Fund | USD 30.65 -1.15 -3.62% |
The Polynomial Regression forecast shown here for ALGER SPECTRA is reference data produced from its historical price series. The projected value and error measures below serve as reference information. This data is provided for reference and analytical review. The Polynomial Regression output serves as one input among many for analytical review.
The Polynomial Regression forecasted value of Alger Spectra Fund on the next trading day is expected to be 31.29 with a mean absolute deviation of 0.44 and the sum of the absolute errors of 27.22.A single variable polynomial regression model attempts to put a curve through the ALGER SPECTRA 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 ALGER SPECTRA presents model-generated projections from historical price data for informational purposes. Polynomial Regression Price Forecast For the 28th of March
Given 90 days horizon, the Polynomial Regression forecasted value of Alger Spectra Fund on the next trading day is expected to be 31.29 with a mean absolute deviation of 0.44 , mean absolute percentage error of 0.33 , and the sum of the absolute errors of 27.22 .Please note that although there have been many attempts to predict ALGER 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 ALGER SPECTRA's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Mutual Fund Forecast Pattern
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Forecasted Value
The next-day forecast for Alger Spectra Fund focuses on identifying predictive downside and upside bands that can frame a realistic trading range. No forecasting approach has been shown to beat all others over time. Investors should treat any model output as a guide, not a guarantee.
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 ALGER SPECTRA mutual fund data series using in forecasting. Note that when a statistical model is used to represent ALGER SPECTRA 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 | 118.8248 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.4391 |
| MAPE | Mean absolute percentage error | 0.0131 |
| SAE | Sum of the absolute errors | 27.2221 |
Other Forecasting Options for ALGER SPECTRA
The distribution of ALGER SPECTRA's daily returns is typically non-normal, with fatter tails than a Gaussian model predicts. This can reveal hidden support and resistance zones in ALGER SPECTRA's chart that simple price charts miss. The slope of ALGER SPECTRA's linear regression channel quantifies trend direction and strength over a chosen lookback period. Divergences between OBV and price can foreshadow trend changes in ALGER.ALGER SPECTRA Related Equities
The peer firms below within the Large Growth space can help frame ALGER SPECTRA's pricing and running costs in context. Revenue and margin checks across this group help investors set expectations for ALGER SPECTRA's results. Peer pricing works best when the firms compared share similar business models and end markets. The data below allows side-by-side review across the most common financial metrics.
| Risk & Return | Correlation |
ALGER SPECTRA Market Strength Events
Market strength indicators for ALGER SPECTRA give insight into the mutual fund's responsiveness to broader forces. These indicators are useful for traders seeking optimal timing for positions in Alger Spectra Fund. Market strength analysis for Alger Spectra Fund works best when combined with volume and volatility data. For ALGER SPECTRA, strength indicators are a practical complement to price and fundamental analysis.
| Rate Of Daily Change | 0.96 | |||
| Day Median Price | 30.65 | |||
| Day Typical Price | 30.65 | |||
| Price Action Indicator | -0.58 | |||
| Period Momentum Indicator | -1.15 | |||
| Relative Strength Index | 37.66 |
ALGER SPECTRA Risk Indicators
A thorough review of ALGER SPECTRA's risk indicators is an important first step in forecasting its price. Quantifying the risk involved in ALGER SPECTRA's allows investors to make better decisions about entry, sizing, and hedging. The assessment of ALGER SPECTRA's risk indicators plays a key role in managing investment exposure. Identifying the magnitude of risk in ALGER SPECTRA's provides context to choose between accepting or hedging exposure.
| Mean Deviation | 1.16 | |||
| Standard Deviation | 1.47 | |||
| Variance | 2.15 |
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 ALGER SPECTRA
Coverage intensity for Alger Spectra Fund matters because narrative visibility can influence sentiment, participation, and volatility around the name. 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.