SPDR Dow (Australia) Market Value
| DJRE Etf | 22.36 0.14 0.63% |
| Symbol | SPDR |
SPDR Dow '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 SPDR Dow'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 SPDR Dow.
| 10/28/2024 |
| 11/27/2024 |
If you would invest 0.00 in SPDR Dow on October 28, 2024 and sell it all today you would earn a total of 0.00 from holding SPDR Dow Jones or generate 0.0% return on investment in SPDR Dow over 30 days. SPDR Dow is related to or competes with SPDR SP, Vanguard Total, IShares Core, IShares Core, VanEck Vectors, VanEck Morningstar, and IShares Global. SPDR Dow is entity of Australia. It is traded as Etf on AU exchange. More
SPDR Dow 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 SPDR Dow'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 SPDR Dow Jones upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.9951 | |||
| Information Ratio | (0.07) | |||
| Maximum Drawdown | 6.33 | |||
| Value At Risk | (1.11) | |||
| Potential Upside | 1.27 |
SPDR Dow Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for SPDR Dow's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as SPDR Dow's standard deviation. In reality, there are many statistical measures that can use SPDR Dow historical prices to predict the future SPDR Dow's volatility.| Risk Adjusted Performance | 0.059 | |||
| Jensen Alpha | 0.0146 | |||
| Total Risk Alpha | (0.09) | |||
| Sortino Ratio | (0.06) | |||
| Treynor Ratio | 0.1596 |
SPDR Dow Jones Backtested Returns
Currently, SPDR Dow Jones is very steady. SPDR Dow Jones owns Efficiency Ratio (i.e., Sharpe Ratio) of 0.0803, which indicates the etf had a 0.0803% return per unit of volatility over the last 3 months. We have found thirty technical indicators for SPDR Dow Jones, which you can use to evaluate the volatility of the etf. Please validate SPDR Dow's risk adjusted performance of 0.059, and Coefficient Of Variation of 1335.36 to confirm if the risk estimate we provide is consistent with the expected return of 0.0747%. The entity has a beta of 0.37, which indicates possible diversification benefits within a given portfolio. As returns on the market increase, SPDR Dow's returns are expected to increase less than the market. However, during the bear market, the loss of holding SPDR Dow is expected to be smaller as well.
Auto-correlation | -0.08 |
Very weak reverse predictability
SPDR Dow Jones has very weak reverse predictability. Overlapping area represents the amount of predictability between SPDR Dow time series from 28th of October 2024 to 12th of November 2024 and 12th of November 2024 to 27th 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 SPDR Dow Jones price movement. The serial correlation of -0.08 indicates that barely 8.0% of current SPDR Dow price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.08 | |
| Spearman Rank Test | -0.17 | |
| Residual Average | 0.0 | |
| Price Variance | 0.04 |
SPDR Dow Jones lagged returns against current returns
Autocorrelation, which is SPDR Dow 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 SPDR Dow's etf expected returns. We can calculate the autocorrelation of SPDR Dow returns to help us make a trade decision. For example, suppose you find that SPDR Dow 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 |
SPDR Dow 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 SPDR Dow etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if SPDR Dow etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in SPDR Dow etf over time.
Current vs Lagged Prices |
| Timeline |
SPDR Dow Lagged Returns
When evaluating SPDR Dow's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of SPDR Dow etf have on its future price. SPDR Dow 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, SPDR Dow autocorrelation shows the relationship between SPDR Dow etf current value and its past values and can show if there is a momentum factor associated with investing in SPDR Dow Jones.
Regressed Prices |
| Timeline |
Thematic Opportunities
Explore Investment Opportunities
Check out SPDR Dow Correlation, SPDR Dow Volatility and SPDR Dow Alpha and Beta module to complement your research on SPDR Dow. You can also try the Portfolio File Import module to quickly import all of your third-party portfolios from your local drive in csv format.
SPDR Dow 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.