Dimensional Etf Trust Etf Market Value
| DFGR Etf | 28.34 0.23 0.82% |
| Symbol | Dimensional |
The market value of Dimensional ETF Trust is measured differently than its book value, which is the value of Dimensional that is recorded on the company's balance sheet. Investors also form their own opinion of Dimensional ETF's value that differs from its market value or its book value, called intrinsic value, which is Dimensional 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 Dimensional ETF's market value can be influenced by many factors that don't directly affect Dimensional 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 Dimensional ETF's value and its price as these two are different measures arrived at by different means. Investors typically determine if Dimensional ETF is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Dimensional 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.
Dimensional 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 Dimensional 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 Dimensional ETF.
| 10/29/2024 |
| 11/28/2024 |
If you would invest 0.00 in Dimensional ETF on October 29, 2024 and sell it all today you would earn a total of 0.00 from holding Dimensional ETF Trust or generate 0.0% return on investment in Dimensional ETF over 30 days. Dimensional ETF is related to or competes with Dimensional ETF, Dimensional ETF, Dimensional ETF, Dimensional International, and Dimensional Core. More
Dimensional 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 Dimensional 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 Dimensional ETF Trust upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.8541 | |||
| Information Ratio | (0.14) | |||
| Maximum Drawdown | 3.4 | |||
| Value At Risk | (1.41) | |||
| Potential Upside | 1.12 |
Dimensional ETF Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Dimensional ETF's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Dimensional ETF's standard deviation. In reality, there are many statistical measures that can use Dimensional ETF historical prices to predict the future Dimensional ETF's volatility.| Risk Adjusted Performance | 0.0192 | |||
| Jensen Alpha | (0) | |||
| Total Risk Alpha | (0.11) | |||
| Sortino Ratio | (0.12) | |||
| Treynor Ratio | 0.0955 |
Dimensional ETF Trust Backtested Returns
Currently, Dimensional ETF Trust is very steady. Dimensional ETF Trust secures Sharpe Ratio (or Efficiency) of 0.0349, which denotes the etf had a 0.0349% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for Dimensional ETF Trust, which you can use to evaluate the volatility of the entity. Please confirm Dimensional ETF's Downside Deviation of 0.8541, mean deviation of 0.622, and Coefficient Of Variation of 3991.44 to check if the risk estimate we provide is consistent with the expected return of 0.0273%. The etf shows a Beta (market volatility) of 0.0957, which means not very significant fluctuations relative to the market. As returns on the market increase, Dimensional ETF's returns are expected to increase less than the market. However, during the bear market, the loss of holding Dimensional ETF is expected to be smaller as well.
Auto-correlation | -0.33 |
Poor reverse predictability
Dimensional ETF Trust has poor reverse predictability. Overlapping area represents the amount of predictability between Dimensional ETF time series from 29th of October 2024 to 13th of November 2024 and 13th of November 2024 to 28th of November 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 Dimensional ETF Trust price movement. The serial correlation of -0.33 indicates that nearly 33.0% of current Dimensional ETF price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.33 | |
| Spearman Rank Test | -0.31 | |
| Residual Average | 0.0 | |
| Price Variance | 0.13 |
Dimensional ETF Trust lagged returns against current returns
Autocorrelation, which is Dimensional 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 Dimensional ETF's etf expected returns. We can calculate the autocorrelation of Dimensional ETF returns to help us make a trade decision. For example, suppose you find that Dimensional 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 |
Dimensional 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 Dimensional ETF etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Dimensional ETF etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Dimensional ETF etf over time.
Current vs Lagged Prices |
| Timeline |
Dimensional ETF Lagged Returns
When evaluating Dimensional ETF's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Dimensional ETF etf have on its future price. Dimensional 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, Dimensional ETF autocorrelation shows the relationship between Dimensional ETF etf current value and its past values and can show if there is a momentum factor associated with investing in Dimensional ETF Trust.
Regressed Prices |
| Timeline |
Pair Trading with Dimensional ETF
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 Dimensional ETF 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 Dimensional ETF will appreciate offsetting losses from the drop in the long position's value.Moving together with Dimensional Etf
| 0.85 | REET | iShares Global REIT | PairCorr |
| 0.88 | RWO | SPDR Dow Jones | PairCorr |
| 0.81 | HAUZ | Xtrackers International | PairCorr |
| 0.82 | RWX | SPDR Dow Jones | PairCorr |
| 0.74 | GQRE | FlexShares Global Quality | PairCorr |
Moving against Dimensional Etf
| 0.49 | SPAQ | Horizon Kinetics SPAC | PairCorr |
| 0.48 | PULS | PGIM Ultra Short | PairCorr |
| 0.41 | BUFF | Innovator Laddered | PairCorr |
The ability to find closely correlated positions to Dimensional ETF could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Dimensional ETF 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 Dimensional ETF - 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 Dimensional ETF Trust to buy it.
The correlation of Dimensional ETF 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 Dimensional ETF moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Dimensional ETF Trust 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 Dimensional ETF 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 Dimensional ETF Correlation, Dimensional ETF Volatility and Dimensional ETF Alpha and Beta module to complement your research on Dimensional ETF. You can also try the Companies Directory module to evaluate performance of over 100,000 Stocks, Funds, and ETFs against different fundamentals.
Dimensional 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.