Kinea Renda Fund Forecast - Polynomial Regression

KNRI11 Fund  BRL 134.45  0.85  0.63%   
The Polynomial Regression forecasted value of Kinea Renda Imobiliria on the next trading day is expected to be 133.02 with a mean absolute deviation of 1.22 and the sum of the absolute errors of 74.33. Kinea Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Kinea Renda stock prices and determine the direction of Kinea Renda Imobiliria's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Kinea Renda's historical fundamentals, such as revenue growth or operating cash flow patterns.
  
Kinea Renda polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Kinea Renda Imobiliria as well as the accuracy indicators are determined from the period prices.

Kinea Renda Polynomial Regression Price Forecast For the 29th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Kinea Renda Imobiliria on the next trading day is expected to be 133.02 with a mean absolute deviation of 1.22, mean absolute percentage error of 2.75, and the sum of the absolute errors of 74.33.
Please note that although there have been many attempts to predict Kinea 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 Kinea Renda's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Kinea Renda Fund Forecast Pattern

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Kinea Renda Forecasted Value

In the context of forecasting Kinea Renda's 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. Kinea Renda's downside and upside margins for the forecasting period are 132.05 and 133.98, respectively. We have considered Kinea Renda'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
134.45
132.05
Downside
133.02
Expected Value
133.98
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 Kinea Renda fund data series using in forecasting. Note that when a statistical model is used to represent Kinea Renda 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 Criteria119.121
BiasArithmetic mean of the errors None
MADMean absolute deviation1.2185
MAPEMean absolute percentage error0.0087
SAESum of the absolute errors74.3261
A single variable polynomial regression model attempts to put a curve through the Kinea Renda 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 Kinea Renda

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Kinea Renda Imobiliria. Regardless of method or technology, however, to accurately forecast the fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the 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
133.48134.45135.42
Details
Intrinsic
Valuation
LowRealHigh
126.77127.74147.90
Details
Bollinger
Band Projection (param)
LowMiddleHigh
133.87135.48137.10
Details

Other Forecasting Options for Kinea Renda

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

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

Kinea Renda Imobiliria Technical and Predictive Analytics

The fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Kinea Renda'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 Kinea Renda's current price.

Kinea Renda Market Strength Events

Market strength indicators help investors to evaluate how Kinea Renda fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Kinea Renda shares will generate the highest return on investment. By undertsting and applying Kinea Renda fund market strength indicators, traders can identify Kinea Renda Imobiliria entry and exit signals to maximize returns.

Kinea Renda Risk Indicators

The analysis of Kinea Renda'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 Kinea Renda's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting kinea 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 Kinea Fund

Kinea Renda financial ratios help investors to determine whether Kinea 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 Kinea with respect to the benefits of owning Kinea Renda security.
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