Applied Finance Mutual Fund Forecast - Simple Exponential Smoothing

AFALX Fund  USD 12.01  0.01  0.08%   
The Simple Exponential Smoothing forecasted value of Applied Finance Core on the next trading day is expected to be 12.01 with a mean absolute deviation of 0.06 and the sum of the absolute errors of 3.46. Applied Mutual Fund Forecast is based on your current time horizon.
  
Applied Finance simple exponential smoothing forecast is a very popular model used to produce a smoothed price series. Whereas in simple Moving Average models the past observations for Applied Finance Core are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as Applied Finance Core prices get older.

Applied Finance Simple Exponential Smoothing Price Forecast For the 15th of December 2024

Given 90 days horizon, the Simple Exponential Smoothing forecasted value of Applied Finance Core on the next trading day is expected to be 12.01 with a mean absolute deviation of 0.06, mean absolute percentage error of 0.01, and the sum of the absolute errors of 3.46.
Please note that although there have been many attempts to predict Applied 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 Applied Finance's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Applied Finance Mutual Fund Forecast Pattern

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Applied Finance Forecasted Value

In the context of forecasting Applied Finance'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. Applied Finance's downside and upside margins for the forecasting period are 11.35 and 12.67, respectively. We have considered Applied Finance'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
12.01
12.01
Expected Value
12.67
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of Applied Finance mutual fund data series using in forecasting. Note that when a statistical model is used to represent Applied Finance 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 Criteria111.1878
BiasArithmetic mean of the errors -0.0033
MADMean absolute deviation0.0577
MAPEMean absolute percentage error0.0048
SAESum of the absolute errors3.46
This simple exponential smoothing model begins by setting Applied Finance Core forecast for the second period equal to the observation of the first period. In other words, recent Applied Finance observations are given relatively more weight in forecasting than the older observations.

Predictive Modules for Applied Finance

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Applied Finance Core. 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
11.3512.0112.67
Details
Intrinsic
Valuation
LowRealHigh
11.3612.0212.68
Details

Other Forecasting Options for Applied Finance

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

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

Applied Finance Core 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 Applied Finance'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 Applied Finance's current price.

Applied Finance Market Strength Events

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

Applied Finance Risk Indicators

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

Applied Finance financial ratios help investors to determine whether Applied 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 Applied with respect to the benefits of owning Applied Finance security.
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