CPN Retail Stock Forecast - Simple Exponential Smoothing

CPNREIT Stock  THB 12.20  0.20  1.61%   
The Simple Exponential Smoothing forecasted value of CPN Retail Growth on the next trading day is expected to be 12.20 with a mean absolute deviation of 0.14 and the sum of the absolute errors of 8.43. CPN Stock Forecast is based on your current time horizon.
  
CPN Retail 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 CPN Retail Growth are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as CPN Retail Growth prices get older.

CPN Retail Simple Exponential Smoothing Price Forecast For the 1st of December

Given 90 days horizon, the Simple Exponential Smoothing forecasted value of CPN Retail Growth on the next trading day is expected to be 12.20 with a mean absolute deviation of 0.14, mean absolute percentage error of 0.05, and the sum of the absolute errors of 8.43.
Please note that although there have been many attempts to predict CPN 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 CPN Retail's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

CPN Retail Stock Forecast Pattern

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CPN Retail Forecasted Value

In the context of forecasting CPN Retail'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. CPN Retail's downside and upside margins for the forecasting period are 10.32 and 14.08, respectively. We have considered CPN Retail'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
12.20
12.20
Expected Value
14.08
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 CPN Retail stock data series using in forecasting. Note that when a statistical model is used to represent CPN Retail 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 Criteria113.3315
BiasArithmetic mean of the errors -0.0168
MADMean absolute deviation0.1405
MAPEMean absolute percentage error0.0115
SAESum of the absolute errors8.43
This simple exponential smoothing model begins by setting CPN Retail Growth forecast for the second period equal to the observation of the first period. In other words, recent CPN Retail observations are given relatively more weight in forecasting than the older observations.

Predictive Modules for CPN Retail

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as CPN Retail Growth. 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 CPN Retail'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
10.3212.2014.08
Details
Intrinsic
Valuation
LowRealHigh
8.3410.2213.42
Details

Other Forecasting Options for CPN Retail

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

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

CPN Retail Growth 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 CPN Retail'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 CPN Retail's current price.

CPN Retail Market Strength Events

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

CPN Retail Risk Indicators

The analysis of CPN Retail'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 CPN Retail's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting cpn 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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Additional Tools for CPN Stock Analysis

When running CPN Retail's price analysis, check to measure CPN Retail's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy CPN Retail is operating at the current time. Most of CPN Retail's value examination focuses on studying past and present price action to predict the probability of CPN Retail's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move CPN Retail's price. Additionally, you may evaluate how the addition of CPN Retail to your portfolios can decrease your overall portfolio volatility.