30 Day Fed Commodity Market Value
| ZQUSD Commodity | 95.47 0.01 0.01% |
| Symbol | ZQUSD |
30 Day '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 30 Day's commodity 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 30 Day.
| 12/29/2023 |
| 11/23/2024 |
If you would invest 0.00 in 30 Day on December 29, 2023 and sell it all today you would earn a total of 0.00 from holding 30 Day Fed or generate 0.0% return on investment in 30 Day over 330 days.
30 Day 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 30 Day's commodity 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 30 Day Fed upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.1083 | |||
| Information Ratio | (1.09) | |||
| Maximum Drawdown | 1.07 | |||
| Value At Risk | (0.02) | |||
| Potential Upside | 0.1678 |
30 Day Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for 30 Day's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as 30 Day's standard deviation. In reality, there are many statistical measures that can use 30 Day historical prices to predict the future 30 Day's volatility.| Risk Adjusted Performance | 0.0299 | |||
| Jensen Alpha | (0.0003) | |||
| Total Risk Alpha | (0.01) | |||
| Sortino Ratio | (1.09) | |||
| Treynor Ratio | 0.1106 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of 30 Day's 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.
30 Day Fed Backtested Returns
At this point, 30 Day is very steady. 30 Day Fed retains Efficiency (Sharpe Ratio) of 0.12, which signifies that the commodity had a 0.12% return per unit of price deviation over the last 3 months. We have found twenty-nine technical indicators for 30 Day, which you can use to evaluate the volatility of the entity. Please confirm 30 Day's Standard Deviation of 0.1084, variance of 0.0118, and Market Risk Adjusted Performance of 0.1206 to double-check if the risk estimate we provide is consistent with the expected return of 0.0128%. The entity owns a Beta (Systematic Risk) of 0.0254, which signifies not very significant fluctuations relative to the market. As returns on the market increase, 30 Day's returns are expected to increase less than the market. However, during the bear market, the loss of holding 30 Day is expected to be smaller as well.
Auto-correlation | -0.05 |
Very weak reverse predictability
30 Day Fed has very weak reverse predictability. Overlapping area represents the amount of predictability between 30 Day time series from 29th of December 2023 to 11th of June 2024 and 11th of June 2024 to 23rd 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 30 Day Fed price movement. The serial correlation of -0.05 indicates that only as little as 5.0% of current 30 Day price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.05 | |
| Spearman Rank Test | 0.68 | |
| Residual Average | 0.0 | |
| Price Variance | 0.11 |
30 Day Fed lagged returns against current returns
Autocorrelation, which is 30 Day commodity'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 30 Day's commodity expected returns. We can calculate the autocorrelation of 30 Day returns to help us make a trade decision. For example, suppose you find that 30 Day has exhibited high autocorrelation historically, and you observe that the commodity 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 |
30 Day 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 30 Day commodity is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if 30 Day commodity is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in 30 Day commodity over time.
Current vs Lagged Prices |
| Timeline |
30 Day Lagged Returns
When evaluating 30 Day's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of 30 Day commodity have on its future price. 30 Day 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, 30 Day autocorrelation shows the relationship between 30 Day commodity current value and its past values and can show if there is a momentum factor associated with investing in 30 Day Fed.
Regressed Prices |
| Timeline |