Gmo Sgm Mutual Fund Forecast - Naive Prediction

A naive forecasting model for Gmo Sgm is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Gmo Sgm Major value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period.

Gmo Sgm Naive Prediction Price Forecast For the 24th of November

Given 90 days horizon, the Naive Prediction forecasted value of Gmo Sgm Major on the next trading day is expected to be 23.26 with a mean absolute deviation of 0.21, mean absolute percentage error of 0.41, and the sum of the absolute errors of 13.00.

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Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Gmo Sgm mutual fund data series using in forecasting. Note that when a statistical model is used to represent Gmo Sgm 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. This model is not at all useful as a medium-long range forecasting tool of Gmo Sgm Major. This model is simplistic and is included partly for completeness and partly because of its simplicity. It is unlikely that you'll want to use this model directly to predict Gmo Sgm. Instead, consider using either the moving average model or the more general weighted moving average model with a higher (i.e., greater than 1) number of periods, and possibly a different set of weights.

Predictive Modules for Gmo Sgm

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Gmo Sgm Major. 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 23.33 23.33 23.33 Details

Intrinsic Valuation Low Real High 22.02 22.02 25.66 Details

Bollinger Band Projection (param) Low Middle High 23.25 23.31 23.37 Details

Please note, it is not enough to conduct a financial or market analysis of a single entity such as Gmo Sgm. Your research has to be compared to or analyzed against Gmo Sgm's peers to derive any actionable benefits. When done correctly, Gmo Sgm's 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 Gmo Sgm Major.

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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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