Scharf Fund Mutual Fund Forecast - Naive Prediction

LOGRX Fund  USD 57.63  0.06  0.10%   
The Naive Prediction forecasted value of Scharf Fund Retail on the next trading day is expected to be 57.99 with a mean absolute deviation of 0.32 and the sum of the absolute errors of 19.29. Scharf Mutual Fund Forecast is based on your current time horizon.
  
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 2nd of December

Given 90 days horizon, the Naive Prediction forecasted value of Scharf Fund Retail on the next trading day is expected to be 57.99 with a mean absolute deviation of 0.32, mean absolute percentage error of 0.17, and the sum of the absolute errors of 19.29.
Please note that although there have been many attempts to predict Scharf 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 Scharf Fund's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Scharf Fund Mutual Fund Forecast Pattern

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Scharf Fund Forecasted Value

In the context of forecasting Scharf Fund's 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. Scharf Fund's downside and upside margins for the forecasting period are 57.41 and 58.57, respectively. We have considered Scharf Fund's 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
57.63
57.99
Expected Value
58.57
Upside

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.
AICAkaike Information Criteria116.3329
BiasArithmetic mean of the errors None
MADMean absolute deviation0.3163
MAPEMean absolute percentage error0.0057
SAESum of the absolute errors19.2929
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
LowEstimatedHigh
57.0457.6258.20
Details
Intrinsic
Valuation
LowRealHigh
56.5357.1157.69
Details
Bollinger
Band Projection (param)
LowMiddleHigh
55.8657.0358.20
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.

Scharf Fund 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 Scharf Fund mutual fund to make a market-neutral strategy. Peer analysis of Scharf Fund could also be used in its relative valuation, which is a method of valuing Scharf Fund by comparing valuation metrics with similar companies.
 Risk & Return  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.

Scharf Fund Market Strength Events

Market strength indicators help investors to evaluate how Scharf Fund 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 Scharf Fund shares will generate the highest return on investment. By undertsting and applying Scharf Fund mutual fund market strength indicators, traders can identify Scharf Fund Retail entry and exit signals to maximize returns.

Scharf Fund Risk Indicators

The analysis of Scharf Fund's 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 Scharf Fund's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting scharf 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 Scharf Mutual Fund

Scharf Fund financial ratios help investors to determine whether Scharf 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 Scharf with respect to the benefits of owning Scharf Fund security.
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