Guggenheim Floating Rate Fund Market Value
| GIFCX Fund | USD 24.49 0.01 0.04% |
| Symbol | Guggenheim |
Guggenheim Floating 'What if' Analysis
In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to Guggenheim Floating's mutual fund what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of Guggenheim Floating.
| 02/20/2024 |
| 05/20/2024 |
If you would invest 0.00 in Guggenheim Floating on February 20, 2024 and sell it all today you would earn a total of 0.00 from holding Guggenheim Floating Rate or generate 0.0% return on investment in Guggenheim Floating over 90 days. Guggenheim Floating is related to or competes with Fidelity New, Fidelity Capital, Fidelity Real, HUMANA, Barloworld, Morningstar Unconstrained, and High Yield. The fund normally invests at least 80 percent of its assets in floating rate senior secured syndicated bank loans, floating rate revolving credit facilities, floating rate unsecured loans, floating rate asset backed securities, other floating rate bonds, loans, notes and other securities, fixed income instruments, and derivative instruments that provide exposure to floating rate or variable rate loans, obligations or other securities. More
Guggenheim Floating Upside/Downside Indicators
Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure Guggenheim Floating's mutual fund current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess Guggenheim Floating Rate upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.1797 | |||
| Information Ratio | (0.40) | |||
| Maximum Drawdown | 1.19 | |||
| Value At Risk | (0.08) | |||
| Potential Upside | 0.5819 |
Guggenheim Floating Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Guggenheim Floating's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Guggenheim Floating's standard deviation. In reality, there are many statistical measures that can use Guggenheim Floating historical prices to predict the future Guggenheim Floating's volatility.| Risk Adjusted Performance | 0.0808 | |||
| Jensen Alpha | 0.0212 | |||
| Total Risk Alpha | (0.01) | |||
| Sortino Ratio | (0.38) | |||
| Treynor Ratio | (3.99) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Guggenheim Floating'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.
Guggenheim Floating Rate Backtested Returns
We consider Guggenheim Floating very steady. Guggenheim Floating Rate holds Efficiency (Sharpe) Ratio of 0.17, which attests that the entity had a 0.17% return per unit of risk over the last 3 months. We have found twenty-seven technical indicators for Guggenheim Floating Rate, which you can use to evaluate the volatility of the entity. Please check out Guggenheim Floating's Market Risk Adjusted Performance of (3.98), coefficient of variation of 558.88, and Risk Adjusted Performance of 0.0808 to validate if the risk estimate we provide is consistent with the expected return of 0.0304%. The fund retains a Market Volatility (i.e., Beta) of -0.0052, which attests to not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Guggenheim Floating are expected to decrease at a much lower rate. During the bear market, Guggenheim Floating is likely to outperform the market.
Auto-correlation | 0.37 |
Below average predictability
Guggenheim Floating Rate has below average predictability. Overlapping area represents the amount of predictability between Guggenheim Floating time series from 20th of February 2024 to 5th of April 2024 and 5th of April 2024 to 20th of May 2024. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Guggenheim Floating Rate price movement. The serial correlation of 0.37 indicates that just about 37.0% of current Guggenheim Floating price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.37 | |
| Spearman Rank Test | 0.45 | |
| Residual Average | 0.0 | |
| Price Variance | 0.01 |
Guggenheim Floating Rate lagged returns against current returns
Autocorrelation, which is Guggenheim Floating mutual fund's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting Guggenheim Floating's mutual fund expected returns. We can calculate the autocorrelation of Guggenheim Floating returns to help us make a trade decision. For example, suppose you find that Guggenheim Floating has exhibited high autocorrelation historically, and you observe that the mutual fund is moving up for the past few days. In that case, you can expect the price movement to match the lagging time series.
Current and Lagged Values |
| Timeline |
Guggenheim Floating regressed lagged prices vs. current prices
Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If Guggenheim Floating mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Guggenheim Floating mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Guggenheim Floating mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Guggenheim Floating Lagged Returns
When evaluating Guggenheim Floating's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Guggenheim Floating mutual fund have on its future price. Guggenheim Floating autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, Guggenheim Floating autocorrelation shows the relationship between Guggenheim Floating mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Guggenheim Floating Rate.
Regressed Prices |
| Timeline |
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.Check out Guggenheim Floating Correlation, Guggenheim Floating Volatility and Guggenheim Floating Alpha and Beta module to complement your research on Guggenheim Floating. Note that the Guggenheim Floating Rate information on this page should be used as a complementary analysis to other Guggenheim Floating's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Funds Screener module to find actively-traded funds from around the world traded on over 30 global exchanges.
Guggenheim Floating technical mutual fund analysis exercises models and trading practices based on price and volume transformations, such as the moving averages, relative strength index, regressions, price and return correlations, business cycles, fund market cycles, or different charting patterns.