Managed Volatility Mutual Fund Forecast - Polynomial Regression

BRBPX Fund  USD 10.85  0.00  0.00%   
The Polynomial Regression forecasted value of Managed Volatility Fund on the next trading day is expected to be 10.85 with a mean absolute deviation of 0 and the sum of the absolute errors of 0.14. Managed Mutual Fund Forecast is based on your current time horizon.
  
Managed Volatility polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Managed Volatility Fund as well as the accuracy indicators are determined from the period prices.

Managed Volatility Polynomial Regression Price Forecast For the 26th of December

Given 90 days horizon, the Polynomial Regression forecasted value of Managed Volatility Fund on the next trading day is expected to be 10.85 with a mean absolute deviation of 0, mean absolute percentage error of 0.00000765, and the sum of the absolute errors of 0.14.
Please note that although there have been many attempts to predict Managed 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 Managed Volatility's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Managed Volatility Mutual Fund Forecast Pattern

Backtest Managed VolatilityManaged Volatility Price PredictionBuy or Sell Advice 

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 Managed Volatility mutual fund data series using in forecasting. Note that when a statistical model is used to represent Managed Volatility 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 Criteria106.3297
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0022
MAPEMean absolute percentage error2.0E-4
SAESum of the absolute errors0.1371
A single variable polynomial regression model attempts to put a curve through the Managed Volatility 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 Managed Volatility

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Managed Volatility. 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
10.8210.8510.88
Details
Intrinsic
Valuation
LowRealHigh
10.7610.7911.94
Details
Bollinger
Band Projection (param)
LowMiddleHigh
10.8510.8510.85
Details

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

Managed Volatility Market Strength Events

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

Managed Volatility Risk Indicators

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

Managed Volatility financial ratios help investors to determine whether Managed 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 Managed with respect to the benefits of owning Managed Volatility security.
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