Consumer Products Mutual Fund Forecast - Naive Prediction

RYCIX Fund  USD 49.37  0.04  0.08%   
The Naive Prediction forecasted value of Consumer Products Fund on the next trading day is expected to be 48.31 with a mean absolute deviation of 0.56 and the sum of the absolute errors of 34.18. Consumer Mutual Fund Forecast is based on your current time horizon.
  
A naive forecasting model for Consumer Products is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Consumer Products Fund 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.

Consumer Products Naive Prediction Price Forecast For the 28th of December

Given 90 days horizon, the Naive Prediction forecasted value of Consumer Products Fund on the next trading day is expected to be 48.31 with a mean absolute deviation of 0.56, mean absolute percentage error of 0.46, and the sum of the absolute errors of 34.18.
Please note that although there have been many attempts to predict Consumer 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 Consumer Products' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Consumer Products Mutual Fund Forecast Pattern

Backtest Consumer ProductsConsumer Products Price PredictionBuy or Sell Advice 

Consumer Products Forecasted Value

In the context of forecasting Consumer Products' 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. Consumer Products' downside and upside margins for the forecasting period are 47.42 and 49.21, respectively. We have considered Consumer Products' 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
49.37
48.31
Expected Value
49.21
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 Consumer Products mutual fund data series using in forecasting. Note that when a statistical model is used to represent Consumer Products 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 Criteria117.3298
BiasArithmetic mean of the errors None
MADMean absolute deviation0.5603
MAPEMean absolute percentage error0.0106
SAESum of the absolute errors34.1791
This model is not at all useful as a medium-long range forecasting tool of Consumer Products Fund. 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 Consumer Products. 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 Consumer Products

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Consumer Products. 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
48.4749.3750.27
Details
Intrinsic
Valuation
LowRealHigh
49.1550.0550.95
Details

Other Forecasting Options for Consumer Products

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

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

Consumer Products 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 Consumer Products' 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 Consumer Products' current price.

Consumer Products Market Strength Events

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

Consumer Products Risk Indicators

The analysis of Consumer Products' 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 Consumer Products' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting consumer 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 Consumer Mutual Fund

Consumer Products financial ratios help investors to determine whether Consumer 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 Consumer with respect to the benefits of owning Consumer Products security.
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