AGROT Stock Forecast - Double Exponential Smoothing

AGROT Stock   11.58  0.19  1.61%   
The Double Exponential Smoothing forecasted value of AGROT on the next trading day is expected to be 11.51 with a mean absolute deviation of 0.35 and the sum of the absolute errors of 20.44. Investors can use prediction functions to forecast AGROT's stock prices and determine the direction of AGROT's future trends based on various well-known forecasting models. However, exclusively looking at the historical price movement is usually misleading. We recommend always using this module together with an analysis of AGROT's historical fundamentals, such as revenue growth or operating cash flow patterns. Check out Trending Equities to better understand how to build diversified portfolios. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in estimate.
  
Double exponential smoothing - also known as Holt exponential smoothing is a refinement of the popular simple exponential smoothing model with an additional trending component. Double exponential smoothing model for AGROT works best with periods where there are trends or seasonality.

AGROT Double Exponential Smoothing Price Forecast For the 26th of November

Given 90 days horizon, the Double Exponential Smoothing forecasted value of AGROT on the next trading day is expected to be 11.51 with a mean absolute deviation of 0.35, mean absolute percentage error of 0.22, and the sum of the absolute errors of 20.44.
Please note that although there have been many attempts to predict AGROT Stock 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 AGROT's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

AGROT Stock Forecast Pattern

AGROT Forecasted Value

In the context of forecasting AGROT's Stock 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. AGROT's downside and upside margins for the forecasting period are 8.46 and 14.56, respectively. We have considered AGROT'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
11.58
11.51
Expected Value
14.56
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Double Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of AGROT stock data series using in forecasting. Note that when a statistical model is used to represent AGROT stock, 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 CriteriaHuge
BiasArithmetic mean of the errors 0.0531
MADMean absolute deviation0.3465
MAPEMean absolute percentage error0.0251
SAESum of the absolute errors20.4418
When AGROT prices exhibit either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any AGROT trend in the prices. So in double exponential smoothing past observations are given exponentially smaller weights as the observations get older. In other words, recent AGROT observations are given relatively more weight in forecasting than the older observations.

Predictive Modules for AGROT

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as AGROT. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock 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.
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of AGROT's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.

Other Forecasting Options for AGROT

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

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

AGROT Technical and Predictive Analytics

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

AGROT Market Strength Events

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

AGROT Risk Indicators

The analysis of AGROT'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 AGROT's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting agrot stock 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.

Thematic Opportunities

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Build portfolios using Macroaxis predefined set of investing ideas. Many of Macroaxis investing ideas can easily outperform a given market. Ideas can also be optimized per your risk profile before portfolio origination is invoked. Macroaxis thematic optimization helps investors identify companies most likely to benefit from changes or shifts in various micro-economic or local macro-level trends. Originating optimal thematic portfolios involves aligning investors' personal views, ideas, and beliefs with their actual investments.
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