Guggenheim Macro Mutual Fund Forecast - Double Exponential Smoothing
| GIOSX Fund | USD 24.12 0.02 0.08% |
The Double Exponential Smoothing forecasted value of Guggenheim Macro Opportunities on the next trading day is expected to be 24.11 with a mean absolute deviation of 0.03 and the sum of the absolute errors of 1.65. Guggenheim Mutual Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Guggenheim Macro stock prices and determine the direction of Guggenheim Macro Opportunities's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Guggenheim Macro's historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Guggenheim Macro to cross-verify your projections. Guggenheim |
Most investors in Guggenheim Macro cannot accurately predict what will happen the next trading day because, historically, fund markets tend to be unpredictable and even illogical. Modeling turbulent structures requires applying different statistical methods, techniques, and algorithms to find hidden data structures or patterns within the Guggenheim Macro's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Guggenheim Macro's price structures and extracts relationships that further increase the generated results' accuracy.
Double exponential smoothing - also known as Holt exponential smoothing is a refinement of the popular simple exponential smoothing model with an additional trending component. Double exponential smoothing model for Guggenheim Macro works best with periods where there are trends or seasonality. Guggenheim Macro Double Exponential Smoothing Price Forecast For the 20th of April
Given 90 days horizon, the Double Exponential Smoothing forecasted value of Guggenheim Macro Opportunities on the next trading day is expected to be 24.11 with a mean absolute deviation of 0.03, mean absolute percentage error of 0, and the sum of the absolute errors of 1.65.Please note that although there have been many attempts to predict Guggenheim Mutual 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 Guggenheim Macro's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Guggenheim Macro Mutual Fund Forecast Pattern
| Backtest Guggenheim Macro | Guggenheim Macro Price Prediction | Buy or Sell Advice |
Guggenheim Macro Forecasted Value
In the context of forecasting Guggenheim Macro's Mutual 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. Guggenheim Macro's downside and upside margins for the forecasting period are 23.94 and 24.27, respectively. We have considered Guggenheim Macro'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 Guggenheim Macro mutual fund data series using in forecasting. Note that when a statistical model is used to represent Guggenheim Macro 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 | Huge |
| Bias | Arithmetic mean of the errors | 0.0069 |
| MAD | Mean absolute deviation | 0.028 |
| MAPE | Mean absolute percentage error | 0.0012 |
| SAE | Sum of the absolute errors | 1.6517 |
Predictive Modules for Guggenheim Macro
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Guggenheim Macro Opp. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual 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.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Guggenheim Macro's 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 Guggenheim Macro
For every potential investor in Guggenheim, whether a beginner or expert, Guggenheim Macro's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Guggenheim Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Guggenheim. Basic forecasting techniques help filter out the noise by identifying Guggenheim Macro's price trends.Guggenheim Macro 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 Guggenheim Macro mutual fund to make a market-neutral strategy. Peer analysis of Guggenheim Macro could also be used in its relative valuation, which is a method of valuing Guggenheim Macro by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Guggenheim Macro Opp Technical and Predictive Analytics
The mutual fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Guggenheim Macro'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 Guggenheim Macro's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Guggenheim Macro Market Strength Events
Market strength indicators help investors to evaluate how Guggenheim Macro 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 Macro shares will generate the highest return on investment. By undertsting and applying Guggenheim Macro mutual fund market strength indicators, traders can identify Guggenheim Macro Opportunities entry and exit signals to maximize returns.
| Daily Balance Of Power | (9,223,372,036,855) | |||
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 24.12 | |||
| Day Typical Price | 24.12 | |||
| Price Action Indicator | (0.01) | |||
| Period Momentum Indicator | (0.02) |
Guggenheim Macro Risk Indicators
The analysis of Guggenheim Macro'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 Macro's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting guggenheim mutual 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 | 0.1044 | |||
| Semi Deviation | 0.0341 | |||
| Standard Deviation | 0.1618 | |||
| Variance | 0.0262 | |||
| Downside Variance | 0.0301 | |||
| Semi Variance | 0.0012 | |||
| Expected Short fall | (0.15) |
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.
Building efficient market-beating portfolios requires time, education, and a lot of computing power!
The Portfolio Architect is an AI-driven system that provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for individual and institutional investors.
Try AI Portfolio ArchitectCheck out Historical Fundamental Analysis of Guggenheim Macro to cross-verify your projections. You can also try the Portfolio Analyzer module to portfolio analysis module that provides access to portfolio diagnostics and optimization engine.