Federated Hermes Etf Market Value
| MKTN Etf | 25.73 0.02 0.08% |
| Symbol | Federated |
The market value of Federated Hermes ETF is measured differently than its book value, which is the value of Federated that is recorded on the company's balance sheet. Investors also form their own opinion of Federated Hermes' value that differs from its market value or its book value, called intrinsic value, which is Federated Hermes' true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Federated Hermes' market value can be influenced by many factors that don't directly affect Federated Hermes' underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Federated Hermes' value and its price as these two are different measures arrived at by different means. Investors typically determine if Federated Hermes is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Federated Hermes' price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.
Federated Hermes '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 Federated Hermes' etf 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 Federated Hermes.
| 06/28/2025 |
| 12/25/2025 |
If you would invest 0.00 in Federated Hermes on June 28, 2025 and sell it all today you would earn a total of 0.00 from holding Federated Hermes ETF or generate 0.0% return on investment in Federated Hermes over 180 days. Federated Hermes is related to or competes with First Trust, USCF ETF, ProShares Ultra, Invesco Exchange, Elevation Series, First Trust, and AdvisorShares Hotel. Federated Hermes is entity of United States More
Federated Hermes 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 Federated Hermes' etf 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 Federated Hermes ETF upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.4352 | |||
| Information Ratio | (0.04) | |||
| Maximum Drawdown | 1.6 | |||
| Value At Risk | (0.59) | |||
| Potential Upside | 0.8337 |
Federated Hermes Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Federated Hermes' investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Federated Hermes' standard deviation. In reality, there are many statistical measures that can use Federated Hermes historical prices to predict the future Federated Hermes' volatility.| Risk Adjusted Performance | 0.0832 | |||
| Jensen Alpha | 0.0364 | |||
| Total Risk Alpha | 0.0086 | |||
| Sortino Ratio | (0.04) | |||
| Treynor Ratio | 0.4231 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Federated Hermes' 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.
Federated Hermes ETF Backtested Returns
As of now, Federated Etf is very steady. Federated Hermes ETF secures Sharpe Ratio (or Efficiency) of 0.13, which denotes the etf had a 0.13 % return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Federated Hermes ETF, which you can use to evaluate the volatility of the entity. Please confirm Federated Hermes' Mean Deviation of 0.2786, downside deviation of 0.4352, and Coefficient Of Variation of 774.86 to check if the risk estimate we provide is consistent with the expected return of 0.0522%. The etf shows a Beta (market volatility) of 0.0998, which means not very significant fluctuations relative to the market. As returns on the market increase, Federated Hermes' returns are expected to increase less than the market. However, during the bear market, the loss of holding Federated Hermes is expected to be smaller as well.
Auto-correlation | 0.00 |
No correlation between past and present
Federated Hermes ETF has no correlation between past and present. Overlapping area represents the amount of predictability between Federated Hermes time series from 28th of June 2025 to 26th of September 2025 and 26th of September 2025 to 25th of December 2025. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Federated Hermes ETF price movement. The serial correlation of 0.0 indicates that just 0.0% of current Federated Hermes price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.0 | |
| Spearman Rank Test | -1.0 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Federated Hermes ETF lagged returns against current returns
Autocorrelation, which is Federated Hermes etf'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 Federated Hermes' etf expected returns. We can calculate the autocorrelation of Federated Hermes returns to help us make a trade decision. For example, suppose you find that Federated Hermes has exhibited high autocorrelation historically, and you observe that the etf 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 |
Federated Hermes 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 Federated Hermes etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Federated Hermes etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Federated Hermes etf over time.
Current vs Lagged Prices |
| Timeline |
Federated Hermes Lagged Returns
When evaluating Federated Hermes' market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Federated Hermes etf have on its future price. Federated Hermes 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, Federated Hermes autocorrelation shows the relationship between Federated Hermes etf current value and its past values and can show if there is a momentum factor associated with investing in Federated Hermes ETF.
Regressed Prices |
| Timeline |
Pair Trading with Federated Hermes
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Federated Hermes position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Federated Hermes will appreciate offsetting losses from the drop in the long position's value.The ability to find closely correlated positions to Federated Hermes could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Federated Hermes when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Federated Hermes - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Federated Hermes ETF to buy it.
The correlation of Federated Hermes is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Federated Hermes moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Federated Hermes ETF moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Federated Hermes can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.Check out Federated Hermes Correlation, Federated Hermes Volatility and Federated Hermes Alpha and Beta module to complement your research on Federated Hermes. You can also try the Cryptocurrency Center module to build and monitor diversified portfolio of extremely risky digital assets and cryptocurrency.
Federated Hermes technical etf 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, etf market cycles, or different charting patterns.