Ipath Bloomberg Commodity Etf Market Value
| DJP Etf | USD 31.98 0.15 0.47% |
| Symbol | IPath |
The market value of iPath Bloomberg Commodity is measured differently than its book value, which is the value of IPath that is recorded on the company's balance sheet. Investors also form their own opinion of IPath Bloomberg's value that differs from its market value or its book value, called intrinsic value, which is IPath Bloomberg'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 IPath Bloomberg's market value can be influenced by many factors that don't directly affect IPath Bloomberg'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 IPath Bloomberg's value and its price as these two are different measures arrived at by different means. Investors typically determine if IPath Bloomberg is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, IPath Bloomberg'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.
IPath Bloomberg '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 IPath Bloomberg'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 IPath Bloomberg.
| 07/20/2023 |
| 12/11/2024 |
If you would invest 0.00 in IPath Bloomberg on July 20, 2023 and sell it all today you would earn a total of 0.00 from holding iPath Bloomberg Commodity or generate 0.0% return on investment in IPath Bloomberg over 510 days. IPath Bloomberg is related to or competes with IShares SP, Invesco DB, and Invesco DB. The Dow Jones-UBS Commodity Index Total ReturnService Mark reflects the returns that are potentially available through a... More
IPath Bloomberg 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 IPath Bloomberg'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 iPath Bloomberg Commodity upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.976 | |||
| Information Ratio | (0.02) | |||
| Maximum Drawdown | 4.13 | |||
| Value At Risk | (1.51) | |||
| Potential Upside | 1.63 |
IPath Bloomberg Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for IPath Bloomberg's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as IPath Bloomberg's standard deviation. In reality, there are many statistical measures that can use IPath Bloomberg historical prices to predict the future IPath Bloomberg's volatility.| Risk Adjusted Performance | 0.084 | |||
| Jensen Alpha | 0.1172 | |||
| Total Risk Alpha | (0.05) | |||
| Sortino Ratio | (0.02) | |||
| Treynor Ratio | (0.51) |
iPath Bloomberg Commodity Backtested Returns
Currently, iPath Bloomberg Commodity is very steady. iPath Bloomberg Commodity holds Efficiency (Sharpe) Ratio of 0.0857, which attests that the entity had a 0.0857% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for iPath Bloomberg Commodity, which you can use to evaluate the volatility of the entity. Please check out IPath Bloomberg's Downside Deviation of 0.976, market risk adjusted performance of (0.50), and Risk Adjusted Performance of 0.084 to validate if the risk estimate we provide is consistent with the expected return of 0.0771%. The etf retains a Market Volatility (i.e., Beta) of -0.19, which attests to not very significant fluctuations relative to the market. As returns on the market increase, returns on owning IPath Bloomberg are expected to decrease at a much lower rate. During the bear market, IPath Bloomberg is likely to outperform the market.
Auto-correlation | 0.42 |
Average predictability
iPath Bloomberg Commodity has average predictability. Overlapping area represents the amount of predictability between IPath Bloomberg time series from 20th of July 2023 to 31st of March 2024 and 31st of March 2024 to 11th 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 iPath Bloomberg Commodity price movement. The serial correlation of 0.42 indicates that just about 42.0% of current IPath Bloomberg price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.42 | |
| Spearman Rank Test | 0.48 | |
| Residual Average | 0.0 | |
| Price Variance | 0.91 |
iPath Bloomberg Commodity lagged returns against current returns
Autocorrelation, which is IPath Bloomberg 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 IPath Bloomberg's etf expected returns. We can calculate the autocorrelation of IPath Bloomberg returns to help us make a trade decision. For example, suppose you find that IPath Bloomberg 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 |
IPath Bloomberg 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 IPath Bloomberg etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if IPath Bloomberg etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in IPath Bloomberg etf over time.
Current vs Lagged Prices |
| Timeline |
IPath Bloomberg Lagged Returns
When evaluating IPath Bloomberg's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of IPath Bloomberg etf have on its future price. IPath Bloomberg 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, IPath Bloomberg autocorrelation shows the relationship between IPath Bloomberg etf current value and its past values and can show if there is a momentum factor associated with investing in iPath Bloomberg Commodity.
Regressed Prices |
| Timeline |
Pair Trading with IPath Bloomberg
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 IPath Bloomberg 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 IPath Bloomberg will appreciate offsetting losses from the drop in the long position's value.Moving together with IPath Etf
| 0.91 | PDBC | Invesco Optimum Yield | PairCorr |
| 0.94 | FTGC | First Trust Global | PairCorr |
| 0.9 | DBC | Invesco DB Commodity | PairCorr |
| 0.88 | COMT | iShares GSCI Commodity | PairCorr |
| 0.89 | GSG | iShares SP GSCI | PairCorr |
Moving against IPath Etf
The ability to find closely correlated positions to IPath Bloomberg could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace IPath Bloomberg 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 IPath Bloomberg - 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 iPath Bloomberg Commodity to buy it.
The correlation of IPath Bloomberg 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 IPath Bloomberg moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if iPath Bloomberg Commodity 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 IPath Bloomberg 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 IPath Bloomberg Correlation, IPath Bloomberg Volatility and IPath Bloomberg Alpha and Beta module to complement your research on IPath Bloomberg. You can also try the Sync Your Broker module to sync your existing holdings, watchlists, positions or portfolios from thousands of online brokerage services, banks, investment account aggregators and robo-advisors..
IPath Bloomberg 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.