PACIFIC FUNDS Mutual Fund Forward View - Simple Regression

PLCSX Fund  USD 10.19  -0.02  -0.20%   
This page provides reference data for PACIFIC FUNDS using Simple Regression forecasting. The projected value and error metrics are calculated from available daily price observations.
The Simple Regression forecasted value of Pacific Funds Short on the next trading day is expected to be 10.25 with a mean absolute deviation of 0.02 and the sum of the absolute errors of 0.97.In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as Pacific Funds Short historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data. This Simple Regression reference page for PACIFIC FUNDS presents model-generated projections from historical price data for informational purposes.
Simple Regression model is a single variable regression model that attempts to put a straight line through PACIFIC FUNDS price points. This line is defined by its gradient or slope, and the point at which it intercepts the x-axis. Mathematically, assuming the independent variable is X and the dependent variable is Y, then this line can be represented as: Y = intercept + slope * X.

Simple Regression Price Forecast For the 21st of March

Given 90 days horizon, the Simple Regression forecasted value of Pacific Funds Short on the next trading day is expected to be 10.25 with a mean absolute deviation of 0.02 , mean absolute percentage error of 0.0004 , and the sum of the absolute errors of 0.97 .
Please note that although there have been many attempts to predict PACIFIC 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 PACIFIC FUNDS's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Mutual Fund Forecast Pattern

Backtest PACIFIC FUNDS  PACIFIC FUNDS Price Prediction  Research Analysis  

Forecasted Value

This next-day forecast for Pacific Funds Short uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. Investors should still remember that no empirical framework consistently proves that one family of forecasting models will outperform all other approaches in live markets.
Market Value
10.19
10.25
Expected Value
10.34
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of PACIFIC FUNDS mutual fund data series using in forecasting. Note that when a statistical model is used to represent PACIFIC FUNDS 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.
AICAkaike Information Criteria110.3736
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0159
MAPEMean absolute percentage error0.0016
SAESum of the absolute errors0.9724
In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as Pacific Funds Short historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Other Forecasting Options for PACIFIC FUNDS

For investors considering PACIFIC, PACIFIC FUNDS's price movement is the most direct driver of investment returns. Noise in PACIFIC Mutual Fund price charts can make identifying meaningful trends difficult without dedicated analytical tools.

PACIFIC FUNDS Related Equities

The following equities are related to PACIFIC FUNDS within the Short-Term Bond space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing PACIFIC FUNDS 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

PACIFIC FUNDS Market Strength Events

Market strength indicators for PACIFIC FUNDS provide investors with a view of how the mutual fund performs across different market environments. By analyzing these indicators, traders can determine the best moments to enter or exit positions in Pacific Funds Short.

PACIFIC FUNDS Risk Indicators

A structured analysis of PACIFIC FUNDS's risk indicators is one of the most reliable ways to improve the accuracy of price forecasts. Understanding the risk embedded in PACIFIC FUNDS's allows investors to decide whether to accept, reduce, or hedge their exposure.
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 PACIFIC FUNDS

A coverage review of Pacific Funds Short shows when the security is attracting above-average attention from contributors and market observers. This is most useful when investors want to understand why a security is suddenly drawing more public discussion.

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.