ABF Thailand Etf Forecast - Double Exponential Smoothing
| ABFTH Etf | 1,245 99.00 7.37% |
ABF |
ABF Thailand Double Exponential Smoothing Price Forecast For the 12th of December 2024
Given 90 days horizon, the Double Exponential Smoothing forecasted value of The ABF Thailand on the next trading day is expected to be 1,245 with a mean absolute deviation of 1.68, mean absolute percentage error of 166.12, and the sum of the absolute errors of 99.00.Please note that although there have been many attempts to predict ABF Etf 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 ABF Thailand's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
ABF Thailand Etf Forecast Pattern
ABF Thailand Forecasted Value
In the context of forecasting ABF Thailand's Etf 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. ABF Thailand's downside and upside margins for the forecasting period are 1,244 and 1,246, respectively. We have considered ABF Thailand'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.
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 ABF Thailand etf data series using in forecasting. Note that when a statistical model is used to represent ABF Thailand etf, 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.| AIC | Akaike Information Criteria | Huge |
| Bias | Arithmetic mean of the errors | -1.678 |
| MAD | Mean absolute deviation | 1.678 |
| MAPE | Mean absolute percentage error | 0.0012 |
| SAE | Sum of the absolute errors | 99.0 |
Predictive Modules for ABF Thailand
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as ABF Thailand. Regardless of method or technology, however, to accurately forecast the etf market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the etf 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.Please note, it is not enough to conduct a financial or market analysis of a single entity such as ABF Thailand. Your research has to be compared to or analyzed against ABF Thailand's peers to derive any actionable benefits. When done correctly, ABF Thailand's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in ABF Thailand.Other Forecasting Options for ABF Thailand
For every potential investor in ABF, whether a beginner or expert, ABF Thailand's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. ABF Etf price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in ABF. Basic forecasting techniques help filter out the noise by identifying ABF Thailand's price trends.ABF Thailand 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 ABF Thailand etf to make a market-neutral strategy. Peer analysis of ABF Thailand could also be used in its relative valuation, which is a method of valuing ABF Thailand by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
ABF Thailand Technical and Predictive Analytics
The etf market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of ABF Thailand'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 ABF Thailand's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
ABF Thailand Market Strength Events
Market strength indicators help investors to evaluate how ABF Thailand etf reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading ABF Thailand shares will generate the highest return on investment. By undertsting and applying ABF Thailand etf market strength indicators, traders can identify The ABF Thailand entry and exit signals to maximize returns.
| Daily Balance Of Power | (9,223,372,036,855) | |||
| Rate Of Daily Change | 0.93 | |||
| Day Median Price | 1245.0 | |||
| Day Typical Price | 1245.0 | |||
| Price Action Indicator | (49.50) | |||
| Period Momentum Indicator | (99.00) |
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