Guggenheim High Yield Fund Market Value
SHYSX Fund | USD 9.83 0.01 0.10% |
Symbol | Guggenheim |
Guggenheim High '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 High'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 High.
02/27/2024 |
| 03/28/2024 |
If you would invest 0.00 in Guggenheim High on February 27, 2024 and sell it all today you would earn a total of 0.00 from holding Guggenheim High Yield or generate 0.0% return on investment in Guggenheim High over 30 days. Guggenheim High is related to or competes with USCF Gold, Guggenheim Investment, Guggenheim Market, Guggenheim Risk, Guggenheim Small, Guggenheim Managed, and Guggenheim Managed. The fund invests at least 80 percent of its assets , under normal circumstances, in a broad range of high yield, high risk debt securities rated below the top four long-term rating categories by a nationally recognized statistical rating organization or, if unrated, determined by Security Investors, LLC, also known as Guggenheim Investments , to be of comparable quality . More
Guggenheim High 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 High'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 High Yield upside and downside potential and time the market with a certain degree of confidence.
Downside Deviation | 0.2867 | |||
Information Ratio | (0.53) | |||
Maximum Drawdown | 1.13 | |||
Value At Risk | (0.31) | |||
Potential Upside | 0.3115 |
Guggenheim High Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Guggenheim High's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Guggenheim High's standard deviation. In reality, there are many statistical measures that can use Guggenheim High historical prices to predict the future Guggenheim High's volatility.Risk Adjusted Performance | 0.0712 | |||
Jensen Alpha | 0.021 | |||
Total Risk Alpha | (0.02) | |||
Sortino Ratio | (0.36) | |||
Treynor Ratio | 8.89 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Guggenheim High'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 High Yield Backtested Returns
We consider Guggenheim High very steady. Guggenheim High Yield holds Efficiency (Sharpe) Ratio of 0.1, which attests that the entity had a 0.1% return per unit of risk over the last 3 months. We have found twenty-seven technical indicators for Guggenheim High Yield, which you can use to evaluate the volatility of the entity. Please check out Guggenheim High's Coefficient Of Variation of 618.44, risk adjusted performance of 0.0712, and Market Risk Adjusted Performance of 8.9 to validate if the risk estimate we provide is consistent with the expected return of 0.0183%. The fund retains a Market Volatility (i.e., Beta) of 0.0024, which attests to not very significant fluctuations relative to the market. As returns on the market increase, Guggenheim High's returns are expected to increase less than the market. However, during the bear market, the loss of holding Guggenheim High is expected to be smaller as well.
Auto-correlation | 0.49 |
Average predictability
Guggenheim High Yield has average predictability. Overlapping area represents the amount of predictability between Guggenheim High time series from 27th of February 2024 to 13th of March 2024 and 13th of March 2024 to 28th of March 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 High Yield price movement. The serial correlation of 0.49 indicates that about 49.0% of current Guggenheim High price fluctuation can be explain by its past prices.
Correlation Coefficient | 0.49 | |
Spearman Rank Test | 0.8 | |
Residual Average | 0.0 | |
Price Variance | 0.0 |
Guggenheim High Yield lagged returns against current returns
Autocorrelation, which is Guggenheim High 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 High's mutual fund expected returns. We can calculate the autocorrelation of Guggenheim High returns to help us make a trade decision. For example, suppose you find that Guggenheim High 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 High 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 High mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Guggenheim High 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 High mutual fund over time.
Current vs Lagged Prices |
Timeline |
Guggenheim High Lagged Returns
When evaluating Guggenheim High's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Guggenheim High mutual fund have on its future price. Guggenheim High 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 High autocorrelation shows the relationship between Guggenheim High mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Guggenheim High Yield.
Regressed Prices |
Timeline |
Currently Active Assets on Macroaxis
Check out Guggenheim High Correlation, Guggenheim High Volatility and Guggenheim High Alpha and Beta module to complement your research on Guggenheim High. You can also try the Piotroski F Score module to get Piotroski F Score based on the binary analysis strategy of nine different fundamentals.
Complementary Tools for Guggenheim Mutual Fund analysis
When running Guggenheim High's price analysis, check to measure Guggenheim High's 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 Guggenheim High is operating at the current time. Most of Guggenheim High's value examination focuses on studying past and present price action to predict the probability of Guggenheim High's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Guggenheim High's price. Additionally, you may evaluate how the addition of Guggenheim High to your portfolios can decrease your overall portfolio volatility.
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Guggenheim High 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.