Scharf Fund Mutual Fund Forecast - Naive Prediction

A naive forecasting model for Scharf Fund is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Scharf Fund Retail 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.

Scharf Fund Naive Prediction Price Forecast For the 27th of November

Given 90 days horizon, the Naive Prediction forecasted value of Scharf Fund Retail on the next trading day is expected to be 57.02 with a mean absolute deviation of 0.36, mean absolute percentage error of 0.19, and the sum of the absolute errors of 21.69.

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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 Scharf Fund mutual fund data series using in forecasting. Note that when a statistical model is used to represent Scharf Fund 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 Scharf Fund Retail. 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 Scharf Fund. 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 Scharf Fund

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Scharf Fund Retail. 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 56.58 57.17 57.76 Details

Intrinsic Valuation Low Real High 56.13 56.72 57.31 Details

Bollinger Band Projection (param) Low Middle High 55.57 56.42 57.28 Details

Other Forecasting Options for Scharf Fund

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

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Correlation

Scharf Fund Retail 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 Scharf Fund's 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 Scharf Fund's current price.

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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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