Invesco QQQ Etf Forecast - Simple Moving Average
| QQQ Etf | MXN 10,655 174.85 1.67% |
The Simple Moving Average forecasted value of Invesco QQQ Trust on the next trading day is expected to be 10,568 with a mean absolute deviation of 105.85 and the sum of the absolute errors of 6,245. Invesco Etf Forecast is based on your current time horizon.
Invesco |
Invesco QQQ Simple Moving Average Price Forecast For the 12th of December 2024
Given 90 days horizon, the Simple Moving Average forecasted value of Invesco QQQ Trust on the next trading day is expected to be 10,568 with a mean absolute deviation of 105.85, mean absolute percentage error of 17,868, and the sum of the absolute errors of 6,245.Please note that although there have been many attempts to predict Invesco 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 Invesco QQQ's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Invesco QQQ Etf Forecast Pattern
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Invesco QQQ Forecasted Value
In the context of forecasting Invesco QQQ'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. Invesco QQQ's downside and upside margins for the forecasting period are 10,566 and 10,569, respectively. We have considered Invesco QQQ'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 Simple Moving Average forecasting method's relative quality and the estimations of the prediction error of Invesco QQQ etf data series using in forecasting. Note that when a statistical model is used to represent Invesco QQQ 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 | 124.2255 |
| Bias | Arithmetic mean of the errors | -39.1945 |
| MAD | Mean absolute deviation | 105.853 |
| MAPE | Mean absolute percentage error | 0.0107 |
| SAE | Sum of the absolute errors | 6245.325 |
Predictive Modules for Invesco QQQ
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Invesco QQQ Trust. 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.Other Forecasting Options for Invesco QQQ
For every potential investor in Invesco, whether a beginner or expert, Invesco QQQ's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Invesco Etf price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Invesco. Basic forecasting techniques help filter out the noise by identifying Invesco QQQ's price trends.Invesco QQQ 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 Invesco QQQ etf to make a market-neutral strategy. Peer analysis of Invesco QQQ could also be used in its relative valuation, which is a method of valuing Invesco QQQ by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Invesco QQQ Trust 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 Invesco QQQ'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 Invesco QQQ's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Invesco QQQ Market Strength Events
Market strength indicators help investors to evaluate how Invesco QQQ etf reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Invesco QQQ shares will generate the highest return on investment. By undertsting and applying Invesco QQQ etf market strength indicators, traders can identify Invesco QQQ Trust entry and exit signals to maximize returns.
| Accumulation Distribution | 3.61 | |||
| Daily Balance Of Power | 7.133823 | |||
| Rate Of Daily Change | 1.02 | |||
| Day Median Price | 10657.26 | |||
| Day Typical Price | 10656.5 | |||
| Market Facilitation Index | 0.0156 | |||
| Price Action Indicator | 85.16 | |||
| Period Momentum Indicator | 174.85 | |||
| Relative Strength Index | 59.83 |
Invesco QQQ Risk Indicators
The analysis of Invesco QQQ'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 Invesco QQQ's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting invesco etf 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 | 0.9501 | |||
| Semi Deviation | 1.05 | |||
| Standard Deviation | 1.22 | |||
| Variance | 1.49 | |||
| Downside Variance | 1.77 | |||
| Semi Variance | 1.11 | |||
| Expected Short fall | (0.98) |
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.
Also Currently Popular
Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.Additional Information and Resources on Investing in Invesco Etf
When determining whether Invesco QQQ Trust is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if Invesco Etf is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about Invesco Qqq Trust Etf. Highlighted below are key reports to facilitate an investment decision about Invesco Qqq Trust Etf:Check out Historical Fundamental Analysis of Invesco QQQ to cross-verify your projections. You can also try the Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.