ArcelorMittal Stock Forecast - Simple Exponential Smoothing

ARRD Stock  EUR 22.16  0.23  1.03%   
The Simple Exponential Smoothing forecasted value of ArcelorMittal SA on the next trading day is expected to be 22.16 with a mean absolute deviation of 0.32 and the sum of the absolute errors of 19.27. ArcelorMittal Stock Forecast is based on your current time horizon. We recommend always using this module together with an analysis of ArcelorMittal's historical fundamentals, such as revenue growth or operating cash flow patterns.
  
ArcelorMittal 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 ArcelorMittal SA are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as ArcelorMittal SA prices get older.

ArcelorMittal Simple Exponential Smoothing Price Forecast For the 23rd of December

Given 90 days horizon, the Simple Exponential Smoothing forecasted value of ArcelorMittal SA on the next trading day is expected to be 22.16 with a mean absolute deviation of 0.32, mean absolute percentage error of 0.20, and the sum of the absolute errors of 19.27.
Please note that although there have been many attempts to predict ArcelorMittal 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 ArcelorMittal's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

ArcelorMittal Stock Forecast Pattern

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ArcelorMittal Forecasted Value

In the context of forecasting ArcelorMittal'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. ArcelorMittal's downside and upside margins for the forecasting period are 20.12 and 24.20, respectively. We have considered ArcelorMittal'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
22.16
22.16
Expected Value
24.20
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 ArcelorMittal stock data series using in forecasting. Note that when a statistical model is used to represent ArcelorMittal 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 Criteria114.6597
BiasArithmetic mean of the errors 0.0178
MADMean absolute deviation0.3212
MAPEMean absolute percentage error0.0139
SAESum of the absolute errors19.27
This simple exponential smoothing model begins by setting ArcelorMittal SA forecast for the second period equal to the observation of the first period. In other words, recent ArcelorMittal observations are given relatively more weight in forecasting than the older observations.

Predictive Modules for ArcelorMittal

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as ArcelorMittal SA. 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 ArcelorMittal'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.
Hype
Prediction
LowEstimatedHigh
20.1222.1624.20
Details
Intrinsic
Valuation
LowRealHigh
20.5722.6124.65
Details
Bollinger
Band Projection (param)
LowMiddleHigh
21.8823.4124.95
Details

Other Forecasting Options for ArcelorMittal

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

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

ArcelorMittal SA 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 ArcelorMittal'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 ArcelorMittal's current price.

ArcelorMittal Market Strength Events

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

ArcelorMittal Risk Indicators

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

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Other Information on Investing in ArcelorMittal Stock

ArcelorMittal financial ratios help investors to determine whether ArcelorMittal Stock 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 ArcelorMittal with respect to the benefits of owning ArcelorMittal security.