Loomis Sayles Mutual Fund Forecast - Polynomial Regression

Loomis Sayles polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Loomis Sayles Small as well as the accuracy indicators are determined from the period prices.

Loomis Sayles Polynomial Regression Price Forecast For the 25th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Loomis Sayles Small on the next trading day is expected to be 32.49 with a mean absolute deviation of 0.48, mean absolute percentage error of 0.39, and the sum of the absolute errors of 29.52.

Loomis Sayles Mutual Fund Forecast Pattern

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Loomis Sayles Forecasted Value

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 Loomis Sayles mutual fund data series using in forecasting. Note that when a statistical model is used to represent Loomis Sayles 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. A single variable polynomial regression model attempts to put a curve through the Loomis Sayles 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 + ... + a_m*X^m

Predictive Modules for Loomis Sayles

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Loomis Sayles Small. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual fund market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.

Hype Prediction Low Estimated High 30.90 32.23 33.56 Details

Intrinsic Valuation Low Real High 30.32 31.65 32.98 Details

Bollinger Band Projection (param) Low Middle High 31.59 32.06 32.53 Details

Other Forecasting Options for Loomis Sayles

For every potential investor in Loomis, whether a beginner or expert, Loomis Sayles' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Loomis Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Loomis. Basic forecasting techniques help filter out the noise by identifying Loomis Sayles' price trends.

Loomis Sayles Related Equities

Risk & Return

Correlation

Loomis Sayles Small Technical and Predictive Analytics

The mutual fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Loomis Sayles' price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of Loomis Sayles' current price.

Loomis Sayles Market Strength Events

Loomis Sayles Risk Indicators

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Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.

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