Guggenheim Ultra Mutual Fund Forward View - Simple Moving Average
| GIYAX Fund | USD 10.05 -0.01 -0.1% |
Momentum
Sell Peaked
Oversold | Overbought |
This view frames how Guggenheim Ultra Short responds to recent headlines and peer activity within its market context.
The Simple Moving Average forecasted value of Guggenheim Ultra Short on the next trading day is expected to be 10.05 with a mean absolute deviation of 0.0042 and the sum of the absolute errors of 0.25.Guggenheim Ultra after-hype prediction price | $ 10.05 |
Sentiment indicators are one input among forecasting models, technical signals, analyst estimates, earnings data, and momentum measures.
Guggenheim |
Guggenheim Ultra Additional Predictive Modules
Most predictive techniques to examine Guggenheim price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for Guggenheim using various technical indicators. When you analyze Guggenheim charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Simple Moving Average Price Forecast For the 15th of March 2026
Given 90 days horizon, the Simple Moving Average forecasted value of Guggenheim Ultra Short on the next trading day is expected to be 10.05 with a mean absolute deviation of 0.0042 , mean absolute percentage error of 0.000093 , and the sum of the absolute errors of 0.25 .Please note that although there have been many attempts to predict Guggenheim Mutual Fund prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Guggenheim Ultra's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Mutual Fund Forecast Pattern
| Backtest Guggenheim Ultra | Guggenheim Ultra Price Prediction | Research Analysis |
Forecasted Value
This next-day forecast for Guggenheim Ultra Short uses model performance to estimate practical downside and upside boundaries rather than a single point target alone. Investors should still remember that no empirical framework consistently proves that one family of forecasting models will outperform all other approaches in live markets.
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 Guggenheim Ultra mutual fund data series using in forecasting. Note that when a statistical model is used to represent Guggenheim Ultra mutual 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 | 106.9843 |
| Bias | Arithmetic mean of the errors | -0.002 |
| MAD | Mean absolute deviation | 0.0042 |
| MAPE | Mean absolute percentage error | 4.0E-4 |
| SAE | Sum of the absolute errors | 0.25 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Guggenheim Ultra's price to converge to an average value over time is called mean reversion.
After-Hype Price Density Analysis
As far as predicting the price of Guggenheim Ultra at your current risk attitude, this probability distribution graph shows the chance that the prediction will fall between or within a specific range.
Next price density |
| Expected price to next headline |
Estimiated After-Hype Price Volatility
In the context of predicting Guggenheim Ultra's mutual fund value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on Guggenheim Ultra's historical news coverage.
Current Value
The after-hype framework applied to Guggenheim Ultra Short assumes a 3 months review window and focuses on post-sentiment normalization rather than raw momentum. This view is most useful when investors want to compare sentiment-driven price extension with a more measured post-news scenario.
Price Outlook Analysis
Have you ever been surprised when a price of a Mutual Fund such as Guggenheim Ultra is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading Guggenheim Ultra backward and forwards among themselves. Have you ever observed a lot of a particular company's price movement is driven by press releases or news about the company that has nothing to do with actual earnings? Usually, hype to individual companies acts as price momentum. If not enough favorable publicity is forthcoming, the Fund price eventually runs out of speed. So, the rule of thumb here is that as long as this news hype has nothing to do with immediate earnings, you should pay more attention to it. If you see this tendency with Guggenheim Ultra, there might be something going there, and it might present an excellent short sale opportunity.
| Expected Return | Period Volatility | Hype Elasticity | Related Elasticity | News Density | Related Density | Expected Hype |
0.01 | 0.08 | 0.00 | 0.02 | 0 Events | 1 Events | In a few days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
10.05 | 10.05 | 0.00 |
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Hype Timeline
Guggenheim Ultra Short is currently traded for 10.05. The fund stock is not elastic to its hype. The average elasticity to hype of competition is 0.02. Guggenheim is forecasted not to react to the next headline, with the price staying at about the same level, and average media hype impact volatility is insignificant. The immediate return on the next news is forecasted to be very small, whereas the daily expected return is currently at 0.01%. %. The volatility of related hype on Guggenheim Ultra is about 3.57%, with the expected price after the next announcement by competition of 10.07. Assuming a 90-day horizon the next forecasted press release will be in a few days. Use Historical Fundamental Analysis of Guggenheim Ultra to cross-verify projections for Guggenheim Ultra. The view provides historical context for the projection set.Related Hype Analysis
Having access to credible news sources related to Guggenheim Ultra's direct competition is more important than ever and may enhance your ability to predict Guggenheim Ultra's future price movements. Getting to know how Guggenheim Ultra's peers react to changing market sentiment, related social.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| DXQLX | Direxion Monthly Nasdaq 100 | 0.00 | 0 per month | 1.88 | 0.05 | 2.65 | -3.36 | 19.92 | |
| BPRRX | Boston Partners Longshort | 0.05 | 1 per month | 0.50 | 0.10 | 0.83 | -0.89 | 2.62 | |
| TNUIX | 1290 Unconstrained Bond | 0.01 | 1 per month | 0.00 | 0.1 | 0.47 | -0.48 | 1.66 | |
| LCRIX | Leuthold E Investment | 0.10 | 1 per month | 0.60 | 0.08 | 0.71 | -1.19 | 2.80 | |
| AEDVX | Emerging Markets Debt | 22.37 | 3 per month | 0.21 | 0.20 | 0.43 | -0.53 | 1.27 | |
| QCSGX | Federated Mdt Small | -0.13 | 1 per month | 1.26 | 0.04 | 1.42 | -2.18 | 9.44 | |
| ABHYX | High Yield Municipal Fund | 0.00 | 1 per month | 0.09 | 0.29 | 0.23 | -0.34 | 1.26 | |
| ANTUX | Nt Non US Intrinsic | 0.00 | 0 per month | 0.92 | 0.14 | 1.55 | -1.50 | 10.31 | |
| BTBFX | Boston Trust Asset | 0.00 | 0 per month | 0.00 | 0.01 | 0.67 | -1.01 | 22.47 | |
| MAPIX | Matthews Asia Dividend | 0.01 | 1 per month | 0.89 | 0.18 | 1.62 | -1.31 | 5.91 |
Other Forecasting Options for Guggenheim Ultra
For every potential investor in Guggenheim, whether a beginner or expert, Guggenheim Ultra's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better.Guggenheim Ultra Related Equities
The following equities are related to Guggenheim Ultra within the Ultrashort Bond space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Guggenheim Ultra against peers on metrics such as P/E, margins, and return on equity helps contextualize its positioning and identify relative strengths or weaknesses.
| Risk & Return | Correlation |
Guggenheim Ultra Market Strength Events
Market strength indicators help investors to evaluate how Guggenheim Ultra mutual fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Guggenheim Ultra shares will generate the highest return on.
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 10.05 | |||
| Day Typical Price | 10.05 | |||
| Price Action Indicator | -0.01 | |||
| Period Momentum Indicator | -0.01 |
Guggenheim Ultra Risk Indicators
The analysis of Guggenheim Ultra'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 Guggenheim Ultra's investment and either accepting that risk or mitigating it.
| Mean Deviation | 0.0398 | |||
| Standard Deviation | 0.0812 | |||
| Variance | 0.0066 | |||
| Downside Variance | 0.0119 | |||
| Semi Variance | -0.01 | |||
| Expected Short fall | -0.20 |
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.
Story Coverage note for Guggenheim Ultra
Coverage intensity for Guggenheim Ultra Short matters because narrative visibility can influence sentiment, participation, and volatility around the name. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.
Other Macroaxis Stories
Story coverage on Macroaxis is built for readers who approach markets from different levels of experience but share the same need for disciplined investment context. Used well, these stories become part of a broader workflow built around idea generation, validation, and risk-adjusted portfolio design.