Loomis Sayles Mutual Fund Forecast - 4 Period Moving Average

LCGRX Fund  USD 26.18  0.16  0.61%   
The 4 Period Moving Average forecasted value of Loomis Sayles Small on the next trading day is expected to be 26.08 with a mean absolute deviation of 0.41 and the sum of the absolute errors of 23.15. Loomis Mutual Fund Forecast is based on your current time horizon.
  
A four-period moving average forecast model for Loomis Sayles Small is based on an artificially constructed daily price series in which the value for a given day is replaced by the mean of that value and the values for four preceding and succeeding time periods. This model is best suited to forecast equities with high volatility.

Loomis Sayles 4 Period Moving Average Price Forecast For the 24th of December

Given 90 days horizon, the 4 Period Moving Average forecasted value of Loomis Sayles Small on the next trading day is expected to be 26.08 with a mean absolute deviation of 0.41, mean absolute percentage error of 0.33, and the sum of the absolute errors of 23.15.
Please note that although there have been many attempts to predict Loomis Mutual Fund prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Loomis Sayles' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Loomis Sayles Mutual Fund Forecast Pattern

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Loomis Sayles Forecasted Value

In the context of forecasting Loomis Sayles' Mutual Fund value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. Loomis Sayles' downside and upside margins for the forecasting period are 24.78 and 27.39, respectively. We have considered Loomis Sayles' daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Market Value
26.18
26.08
Expected Value
27.39
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the 4 Period Moving Average 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.
AICAkaike Information Criteria109.6357
BiasArithmetic mean of the errors -0.031
MADMean absolute deviation0.4062
MAPEMean absolute percentage error0.0153
SAESum of the absolute errors23.1525
The four period moving average method has an advantage over other forecasting models in that it does smooth out peaks and troughs in a set of daily price observations of Loomis Sayles. However, it also has several disadvantages. In particular this model does not produce an actual prediction equation for Loomis Sayles Small and therefore, it cannot be a useful forecasting tool for medium or long range price predictions

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
LowEstimatedHigh
24.8926.1827.47
Details
Intrinsic
Valuation
LowRealHigh
24.9726.2627.55
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

One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with Loomis Sayles mutual fund to make a market-neutral strategy. Peer analysis of Loomis Sayles could also be used in its relative valuation, which is a method of valuing Loomis Sayles by comparing valuation metrics with similar companies.
 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

Market strength indicators help investors to evaluate how Loomis Sayles mutual fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Loomis Sayles shares will generate the highest return on investment. By undertsting and applying Loomis Sayles mutual fund market strength indicators, traders can identify Loomis Sayles Small entry and exit signals to maximize returns.

Loomis Sayles Risk Indicators

The analysis of Loomis Sayles' basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Loomis Sayles' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting loomis mutual fund prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
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.

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.

Other Information on Investing in Loomis Mutual Fund

Loomis Sayles financial ratios help investors to determine whether Loomis Mutual Fund is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Loomis with respect to the benefits of owning Loomis Sayles security.
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