Dbx Etf Trust Etf Market Value
| SNPD Etf | 28.26 0.08 0.28% |
| Symbol | DBX |
The market value of DBX ETF Trust is measured differently than its book value, which is the value of DBX that is recorded on the company's balance sheet. Investors also form their own opinion of DBX ETF's value that differs from its market value or its book value, called intrinsic value, which is DBX ETF's 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 DBX ETF's market value can be influenced by many factors that don't directly affect DBX ETF's 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 DBX ETF's value and its price as these two are different measures arrived at by different means. Investors typically determine if DBX ETF is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, DBX ETF's 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.
DBX ETF '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 DBX ETF's 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 DBX ETF.
| 11/02/2024 |
| 12/02/2024 |
If you would invest 0.00 in DBX ETF on November 2, 2024 and sell it all today you would earn a total of 0.00 from holding DBX ETF Trust or generate 0.0% return on investment in DBX ETF over 30 days. DBX ETF is related to or competes with Vanguard Mid, SPDR SP, SCOR PK, HUMANA, Barloworld, Morningstar Unconstrained, and High-yield Municipal. Southern Products, Inc., a consumer electronics company, engages in the design, assembly, import, marketing, and sale of flat panel televisions under the Sigmac brand name. More
DBX ETF 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 DBX ETF's 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 DBX ETF Trust upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.565 | |||
| Information Ratio | (0.14) | |||
| Maximum Drawdown | 2.2 | |||
| Value At Risk | (0.84) | |||
| Potential Upside | 1.1 |
DBX ETF Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for DBX ETF's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as DBX ETF's standard deviation. In reality, there are many statistical measures that can use DBX ETF historical prices to predict the future DBX ETF's volatility.| Risk Adjusted Performance | 0.0664 | |||
| Jensen Alpha | (0.03) | |||
| Total Risk Alpha | (0.06) | |||
| Sortino Ratio | (0.15) | |||
| Treynor Ratio | 0.0724 |
DBX ETF Trust Backtested Returns
At this point, DBX ETF is very steady. DBX ETF Trust secures Sharpe Ratio (or Efficiency) of 0.0794, which denotes the etf had a 0.0794% return per unit of volatility over the last 3 months. We have found twenty-seven technical indicators for DBX ETF Trust, which you can use to evaluate the volatility of the entity. Please confirm DBX ETF's Market Risk Adjusted Performance of 0.0824, downside deviation of 0.565, and Mean Deviation of 0.5076 to check if the risk estimate we provide is consistent with the expected return of 0.0472%. The entity shows a Beta (market volatility) of 0.62, which means possible diversification benefits within a given portfolio. As returns on the market increase, DBX ETF's returns are expected to increase less than the market. However, during the bear market, the loss of holding DBX ETF is expected to be smaller as well.
Auto-correlation | 0.03 |
Virtually no predictability
DBX ETF Trust has virtually no predictability. Overlapping area represents the amount of predictability between DBX ETF time series from 2nd of November 2024 to 17th of November 2024 and 17th of November 2024 to 2nd of December 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 DBX ETF Trust price movement. The serial correlation of 0.03 indicates that only 3.0% of current DBX ETF price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.03 | |
| Spearman Rank Test | 0.02 | |
| Residual Average | 0.0 | |
| Price Variance | 0.18 |
DBX ETF Trust lagged returns against current returns
Autocorrelation, which is DBX ETF 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 DBX ETF's etf expected returns. We can calculate the autocorrelation of DBX ETF returns to help us make a trade decision. For example, suppose you find that DBX ETF 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 |
DBX ETF 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 DBX ETF etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if DBX ETF etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in DBX ETF etf over time.
Current vs Lagged Prices |
| Timeline |
DBX ETF Lagged Returns
When evaluating DBX ETF's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of DBX ETF etf have on its future price. DBX ETF 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, DBX ETF autocorrelation shows the relationship between DBX ETF etf current value and its past values and can show if there is a momentum factor associated with investing in DBX ETF Trust.
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.When determining whether DBX ETF Trust offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of DBX ETF's financial statements, including income statements, balance sheets, and cash flow statements, to assess its financial health. Key financial ratios are used to gauge profitability, efficiency, and growth potential of Dbx Etf Trust Etf. Outlined below are crucial reports that will aid in making a well-informed decision on Dbx Etf Trust Etf:Check out DBX ETF Correlation, DBX ETF Volatility and DBX ETF Alpha and Beta module to complement your research on DBX ETF. You can also try the Idea Breakdown module to analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes.
DBX ETF 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.