Plastic Additives Stock Forecast - Naive Prediction

PGN Stock   10,800  400.00  3.85%   
The Naive Prediction forecasted value of Plastic Additives JSC on the next trading day is expected to be 9,555 with a mean absolute deviation of 408.67 and the sum of the absolute errors of 24,929. Investors can use prediction functions to forecast Plastic Additives' stock prices and determine the direction of Plastic Additives JSC'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 Plastic Additives' historical fundamentals, such as revenue growth or operating cash flow patterns. Check out Your Equity Center 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 board of governors.
  
A naive forecasting model for Plastic Additives is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Plastic Additives JSC value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period.

Plastic Additives Naive Prediction Price Forecast For the 30th of November

Given 90 days horizon, the Naive Prediction forecasted value of Plastic Additives JSC on the next trading day is expected to be 9,555 with a mean absolute deviation of 408.67, mean absolute percentage error of 270,295, and the sum of the absolute errors of 24,929.
Please note that although there have been many attempts to predict Plastic 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 Plastic Additives' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Plastic Additives Stock Forecast Pattern

Plastic Additives Forecasted Value

In the context of forecasting Plastic Additives' 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. Plastic Additives' downside and upside margins for the forecasting period are 9,551 and 9,560, respectively. We have considered Plastic Additives' 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
10,800
9,555
Expected Value
9,560
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Plastic Additives stock data series using in forecasting. Note that when a statistical model is used to represent Plastic Additives 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 Criteria130.6178
BiasArithmetic mean of the errors None
MADMean absolute deviation408.6706
MAPEMean absolute percentage error0.0493
SAESum of the absolute errors24928.9038
This model is not at all useful as a medium-long range forecasting tool of Plastic Additives JSC. This model is simplistic and is included partly for completeness and partly because of its simplicity. It is unlikely that you'll want to use this model directly to predict Plastic Additives. Instead, consider using either the moving average model or the more general weighted moving average model with a higher (i.e., greater than 1) number of periods, and possibly a different set of weights.

Predictive Modules for Plastic Additives

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Plastic Additives JSC. 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.

Other Forecasting Options for Plastic Additives

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

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

Plastic Additives JSC 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 Plastic Additives' 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 Plastic Additives' current price.

Plastic Additives Market Strength Events

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

Plastic Additives Risk Indicators

The analysis of Plastic Additives' 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 Plastic Additives' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting plastic 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.

Pair Trading with Plastic Additives

One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Plastic Additives position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Plastic Additives will appreciate offsetting losses from the drop in the long position's value.
The ability to find closely correlated positions to Plastic Additives could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Plastic Additives when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Plastic Additives - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Plastic Additives JSC to buy it.
The correlation of Plastic Additives is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Plastic Additives moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Plastic Additives JSC moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Plastic Additives can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.
Pair CorrelationCorrelation Matching