Energy Fuels Stock Forecast - Triple Exponential Smoothing
| UUUU Stock | USD 6.41 0.04 0.63% |
The Triple Exponential Smoothing forecasted value of Energy Fuels on the next trading day is expected to be 6.47 with a mean absolute deviation of 0.19 and the sum of the absolute errors of 11.24. Energy Stock Forecast is based on your current time horizon.
Energy |
Energy Fuels Triple Exponential Smoothing Price Forecast For the 12th of December 2024
Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Energy Fuels on the next trading day is expected to be 6.47 with a mean absolute deviation of 0.19, mean absolute percentage error of 0.06, and the sum of the absolute errors of 11.24.Please note that although there have been many attempts to predict Energy 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 Energy Fuels' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Energy Fuels Stock Forecast Pattern
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Energy Fuels Forecasted Value
In the context of forecasting Energy Fuels' 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. Energy Fuels' downside and upside margins for the forecasting period are 2.46 and 10.48, respectively. We have considered Energy Fuels' 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 Triple Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of Energy Fuels stock data series using in forecasting. Note that when a statistical model is used to represent Energy Fuels 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.| AIC | Akaike Information Criteria | Huge |
| Bias | Arithmetic mean of the errors | 0.032 |
| MAD | Mean absolute deviation | 0.1873 |
| MAPE | Mean absolute percentage error | 0.03 |
| SAE | Sum of the absolute errors | 11.24 |
Predictive Modules for Energy Fuels
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Energy Fuels. 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 Energy Fuels' 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.
Other Forecasting Options for Energy Fuels
For every potential investor in Energy, whether a beginner or expert, Energy Fuels' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Energy Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Energy. Basic forecasting techniques help filter out the noise by identifying Energy Fuels' price trends.Energy Fuels 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 Energy Fuels stock to make a market-neutral strategy. Peer analysis of Energy Fuels could also be used in its relative valuation, which is a method of valuing Energy Fuels by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Energy Fuels 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 Energy Fuels' 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 Energy Fuels' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Energy Fuels Market Strength Events
Market strength indicators help investors to evaluate how Energy Fuels stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Energy Fuels shares will generate the highest return on investment. By undertsting and applying Energy Fuels stock market strength indicators, traders can identify Energy Fuels entry and exit signals to maximize returns.
| Accumulation Distribution | 96542.75 | |||
| Daily Balance Of Power | 0.2105 | |||
| Rate Of Daily Change | 1.01 | |||
| Day Median Price | 6.4 | |||
| Day Typical Price | 6.4 | |||
| Price Action Indicator | 0.035 | |||
| Period Momentum Indicator | 0.04 |
Energy Fuels Risk Indicators
The analysis of Energy Fuels' 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 Energy Fuels' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting energy 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.
| Mean Deviation | 3.13 | |||
| Semi Deviation | 2.85 | |||
| Standard Deviation | 4.11 | |||
| Variance | 16.87 | |||
| Downside Variance | 10.5 | |||
| Semi Variance | 8.11 | |||
| Expected Short fall | (3.90) |
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.
Thematic Opportunities
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Additional Tools for Energy Stock Analysis
When running Energy Fuels' price analysis, check to measure Energy Fuels' 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 Energy Fuels is operating at the current time. Most of Energy Fuels' value examination focuses on studying past and present price action to predict the probability of Energy Fuels' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Energy Fuels' price. Additionally, you may evaluate how the addition of Energy Fuels to your portfolios can decrease your overall portfolio volatility.