Federated Kaufmann Mutual Fund Forecast - Polynomial Regression

KLCIX Fund  USD 25.10  0.11  0.44%   
The Polynomial Regression forecasted value of Federated Kaufmann Large on the next trading day is expected to be 25.25 with a mean absolute deviation of 0.25 and the sum of the absolute errors of 15.28. Federated Mutual Fund Forecast is based on your current time horizon.
  
Federated Kaufmann polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Federated Kaufmann Large as well as the accuracy indicators are determined from the period prices.

Federated Kaufmann Polynomial Regression Price Forecast For the 25th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Federated Kaufmann Large on the next trading day is expected to be 25.25 with a mean absolute deviation of 0.25, mean absolute percentage error of 0.10, and the sum of the absolute errors of 15.28.
Please note that although there have been many attempts to predict Federated 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 Federated Kaufmann's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Federated Kaufmann Mutual Fund Forecast Pattern

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Federated Kaufmann Forecasted Value

In the context of forecasting Federated Kaufmann'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. Federated Kaufmann's downside and upside margins for the forecasting period are 24.27 and 26.22, respectively. We have considered Federated Kaufmann'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
25.10
25.25
Expected Value
26.22
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of Federated Kaufmann mutual fund data series using in forecasting. Note that when a statistical model is used to represent Federated Kaufmann 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 Criteria115.8227
BiasArithmetic mean of the errors None
MADMean absolute deviation0.2505
MAPEMean absolute percentage error0.0105
SAESum of the absolute errors15.2818
A single variable polynomial regression model attempts to put a curve through the Federated Kaufmann historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm

Predictive Modules for Federated Kaufmann

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Federated Kaufmann Large. 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
24.1325.1026.07
Details
Intrinsic
Valuation
LowRealHigh
23.7724.7425.71
Details
Bollinger
Band Projection (param)
LowMiddleHigh
24.9625.0625.17
Details

Other Forecasting Options for Federated Kaufmann

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

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

Federated Kaufmann Large 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 Federated Kaufmann'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 Federated Kaufmann's current price.

Federated Kaufmann Market Strength Events

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

Federated Kaufmann Risk Indicators

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

Federated Kaufmann financial ratios help investors to determine whether Federated 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 Federated with respect to the benefits of owning Federated Kaufmann security.
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