Loomis Sayles Mutual Fund Forecast - Simple Regression

Simple Regression model is a single variable regression model that attempts to put a straight line through Loomis Sayles 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.

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

Given 90 days horizon, the Simple Regression forecasted value of Loomis Sayles Small on the next trading day is expected to be 22.38 with a mean absolute deviation of 0.38, mean absolute percentage error of 0.23, and the sum of the absolute errors of 23.21.

Loomis Sayles Mutual Fund Forecast Pattern

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Loomis Sayles Price Prediction

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Loomis Sayles Forecasted Value

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 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. 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 Loomis Sayles Small historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

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 21.81 23.04 24.27 Details

Intrinsic Valuation Low Real High 21.45 22.68 23.91 Details

Bollinger Band Projection (param) Low Middle High 22.57 22.92 23.27 Details

Please note, it is not enough to conduct a financial or market analysis of a single entity such as Loomis Sayles. Your research has to be compared to or analyzed against Loomis Sayles' peers to derive any actionable benefits. When done correctly, Loomis Sayles' competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in Loomis Sayles Small.

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

Also Currently Popular

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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