ALM ES Fund Forecast - 20 Period Moving Average
| 0P0001NBQF | 117.50 0.00 0.00% |
ALM |
ALM ES 20 Period Moving Average Price Forecast For the 19th of March
Given 90 days horizon, the 20 Period Moving Average forecasted value of ALM ES Actions on the next trading day is expected to be 125.70 with a mean absolute deviation of 2.92, mean absolute percentage error of 17.61, and the sum of the absolute errors of 119.70.Please note that although there have been many attempts to predict ALM Fund 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 ALM ES's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
ALM ES Fund Forecast Pattern
ALM ES Forecasted Value
In the context of forecasting ALM ES's Fund 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. ALM ES's downside and upside margins for the forecasting period are 124.85 and 126.55, respectively. We have considered ALM ES'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 20 Period Moving Average forecasting method's relative quality and the estimations of the prediction error of ALM ES fund data series using in forecasting. Note that when a statistical model is used to represent ALM ES fund, 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 | 84.2212 |
| Bias | Arithmetic mean of the errors | 1.2016 |
| MAD | Mean absolute deviation | 2.9195 |
| MAPE | Mean absolute percentage error | 0.0236 |
| SAE | Sum of the absolute errors | 119.698 |
Predictive Modules for ALM ES
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as ALM ES Actions. Regardless of method or technology, however, to accurately forecast the fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the fund 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 ALM ES
For every potential investor in ALM, whether a beginner or expert, ALM ES's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. ALM Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in ALM. Basic forecasting techniques help filter out the noise by identifying ALM ES's price trends.ALM ES 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 ALM ES fund to make a market-neutral strategy. Peer analysis of ALM ES could also be used in its relative valuation, which is a method of valuing ALM ES by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
ALM ES Actions Technical and Predictive Analytics
The fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of ALM ES'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 ALM ES's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
ALM ES Market Strength Events
Market strength indicators help investors to evaluate how ALM ES fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading ALM ES shares will generate the highest return on investment. By undertsting and applying ALM ES fund market strength indicators, traders can identify ALM ES Actions entry and exit signals to maximize returns.
ALM ES Risk Indicators
The analysis of ALM ES'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 ALM ES's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting alm fund 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.
| Mean Deviation | 0.596 | |||
| Standard Deviation | 0.8007 | |||
| Variance | 0.6412 |
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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