T Rowe Mutual Fund Forward View - Simple Regression

TEUIX Fund  USD 23.62  0.32  1.37%   
The Simple Regression forecast shown here for T Rowe 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 Simple Regression output serves as one input among many for analytical review.
The Simple Regression forecasted value of T Rowe Price on the next trading day is expected to be 24.35 with a mean absolute deviation of 0.61 and the sum of the absolute errors of 37.52.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 T Rowe Price 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 T Rowe 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 T Rowe 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 27th of March

Given 90 days horizon, the Simple Regression forecasted value of T Rowe Price on the next trading day is expected to be 24.35 with a mean absolute deviation of 0.61 , mean absolute percentage error of 0.50 , and the sum of the absolute errors of 37.52 .
Please note that although there have been many attempts to predict TEUIX 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 T Rowe'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

This next-day forecast for T Rowe Price uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. At the moment, the model places downside around 23.28 and upside around 25.43 for the forecasting period.
Market Value
23.62
24.35
Expected Value
25.43
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 T Rowe mutual fund data series using in forecasting. Note that when a statistical model is used to represent T Rowe 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 Criteria119.2635
BiasArithmetic mean of the errors None
MADMean absolute deviation0.6052
MAPEMean absolute percentage error0.0242
SAESum of the absolute errors37.522
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 T Rowe Price 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 T Rowe

The distribution of T Rowe's daily returns is typically non-normal, with fatter tails than a Gaussian model predicts. This can reveal hidden support and resistance zones in T Rowe's chart that simple price charts miss. The slope of T Rowe's linear regression channel quantifies trend direction and strength over a chosen lookback period. Divergences between OBV and price can foreshadow trend changes in TEUIX.

T Rowe Related Equities

These stocks within the Europe Stock space are often compared to T Rowe by analysts and fund managers in the sector. Checking cash flow across this peer set helps gauge T Rowe's relative financial strength. Peer review is most useful when paired with absolute pricing and trend checks. Combining quantitative ratios with qualitative context such as management quality and market position sharpens peer comparisons.
 Risk & Return  Correlation

T Rowe Market Strength Events

Market strength indicators for T Rowe give insight into the mutual fund's responsiveness to broader forces. These indicators are useful for traders seeking optimal timing for positions in T Rowe Price. Market strength analysis for T Rowe Price works best when combined with volume and volatility data. For T Rowe, strength indicators are a practical complement to price and fundamental analysis.

T Rowe Risk Indicators

A thorough review of T Rowe's risk indicators is an important first step in forecasting its price. Quantifying the risk involved in T Rowe's allows investors to make better decisions about entry, sizing, and hedging. The assessment of T Rowe's risk indicators plays a key role in managing investment exposure. Identifying the magnitude of risk in T Rowe's provides context to choose between accepting or hedging 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 T Rowe

Story coverage around T Rowe Price often expands when market conditions, narrative momentum, or risk-adjusted performance make the security more visible to investors. This is most useful when investors want to understand why a security is suddenly drawing more public discussion.

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.