Fidelity Short Term Treasury Fund Market Value
| FUMBX Fund | USD 9.97 0.01 0.10% |
| Symbol | Fidelity |
Fidelity Short '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 Fidelity Short's mutual fund 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 Fidelity Short.
| 03/20/2024 |
| 04/19/2024 |
If you would invest 0.00 in Fidelity Short on March 20, 2024 and sell it all today you would earn a total of 0.00 from holding Fidelity Short Term Treasury or generate 0.0% return on investment in Fidelity Short over 30 days. Fidelity Short is related to or competes with Fidelity Intermediate, Fidelity Long, Fidelity Short-term, Fidelity Inflation, and Fidelity Large. The fund normally invests at least 80 percent of assets in securities included in the Bloomberg U.S More
Fidelity Short 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 Fidelity Short's mutual fund 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 Fidelity Short Term Treasury upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.44) | |||
| Maximum Drawdown | 1.1 | |||
| Value At Risk | (0.30) | |||
| Potential Upside | 0.2002 |
Fidelity Short Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Fidelity Short's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Fidelity Short's standard deviation. In reality, there are many statistical measures that can use Fidelity Short historical prices to predict the future Fidelity Short's volatility.| Risk Adjusted Performance | (0.07) | |||
| Jensen Alpha | (0.03) | |||
| Total Risk Alpha | (0.04) | |||
| Treynor Ratio | (0.26) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Fidelity Short'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.
Fidelity Short Term Backtested Returns
Fidelity Short Term secures Sharpe Ratio (or Efficiency) of -0.0577, which denotes the fund had a -0.0577% return per unit of risk over the last 3 months. Fidelity Short Term Treasury exposes twenty-one different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Fidelity Short's Variance of 0.0354, standard deviation of 0.1883, and Mean Deviation of 0.1411 to check the risk estimate we provide. The fund shows a Beta (market volatility) of 0.0947, which means not very significant fluctuations relative to the market. As returns on the market increase, Fidelity Short's returns are expected to increase less than the market. However, during the bear market, the loss of holding Fidelity Short is expected to be smaller as well.
Auto-correlation | -0.51 |
Good reverse predictability
Fidelity Short Term Treasury has good reverse predictability. Overlapping area represents the amount of predictability between Fidelity Short time series from 20th of March 2024 to 4th of April 2024 and 4th of April 2024 to 19th of April 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 Fidelity Short Term price movement. The serial correlation of -0.51 indicates that about 51.0% of current Fidelity Short price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.51 | |
| Spearman Rank Test | -0.65 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Fidelity Short Term lagged returns against current returns
Autocorrelation, which is Fidelity Short mutual fund'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 Fidelity Short's mutual fund expected returns. We can calculate the autocorrelation of Fidelity Short returns to help us make a trade decision. For example, suppose you find that Fidelity Short has exhibited high autocorrelation historically, and you observe that the mutual fund 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 |
Fidelity Short 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 Fidelity Short mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Fidelity Short mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Fidelity Short mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Fidelity Short Lagged Returns
When evaluating Fidelity Short's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Fidelity Short mutual fund have on its future price. Fidelity Short 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, Fidelity Short autocorrelation shows the relationship between Fidelity Short mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Fidelity Short Term Treasury.
Regressed Prices |
| Timeline |
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards Fidelity Short in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, Fidelity Short's short interest history, or implied volatility extrapolated from Fidelity Short options trading.
Building efficient market-beating portfolios requires time, education, and a lot of computing power!
The Portfolio Architect is an AI-driven system that provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for individual and institutional investors.
Try AI Portfolio ArchitectCheck out Fidelity Short Correlation, Fidelity Short Volatility and Fidelity Short Alpha and Beta module to complement your research on Fidelity Short. Note that the Fidelity Short Term information on this page should be used as a complementary analysis to other Fidelity Short's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Fundamentals Comparison module to compare fundamentals across multiple equities to find investing opportunities.
Fidelity Short technical mutual fund 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, fund market cycles, or different charting patterns.