Fidelity Momentum Factor Etf Market Value
| FDMO Etf | USD 72.26 0.10 0.14% |
| Symbol | Fidelity |
The market value of Fidelity Momentum Factor is measured differently than its book value, which is the value of Fidelity that is recorded on the company's balance sheet. Investors also form their own opinion of Fidelity Momentum's value that differs from its market value or its book value, called intrinsic value, which is Fidelity Momentum'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 Fidelity Momentum's market value can be influenced by many factors that don't directly affect Fidelity Momentum'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 Fidelity Momentum's value and its price as these two are different measures arrived at by different means. Investors typically determine if Fidelity Momentum is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Fidelity Momentum'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.
Fidelity Momentum '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 Momentum'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 Fidelity Momentum.
| 06/19/2024 |
| 12/16/2024 |
If you would invest 0.00 in Fidelity Momentum on June 19, 2024 and sell it all today you would earn a total of 0.00 from holding Fidelity Momentum Factor or generate 0.0% return on investment in Fidelity Momentum over 180 days. Fidelity Momentum is related to or competes with Absolute Core, IShares ESG, and PIMCO RAFI. The fund normally invests at least 80 percent of assets in securities included in the Fidelity U.S More
Fidelity Momentum 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 Momentum'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 Fidelity Momentum Factor upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.8304 | |||
| Information Ratio | 0.1113 | |||
| Maximum Drawdown | 4.35 | |||
| Value At Risk | (1.25) | |||
| Potential Upside | 1.7 |
Fidelity Momentum Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Fidelity Momentum's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Fidelity Momentum's standard deviation. In reality, there are many statistical measures that can use Fidelity Momentum historical prices to predict the future Fidelity Momentum's volatility.| Risk Adjusted Performance | 0.167 | |||
| Jensen Alpha | 0.1102 | |||
| Total Risk Alpha | 0.079 | |||
| Sortino Ratio | 0.1149 | |||
| Treynor Ratio | 0.2219 |
Fidelity Momentum Factor Backtested Returns
As of now, Fidelity Etf is very steady. Fidelity Momentum Factor secures Sharpe Ratio (or Efficiency) of 0.2, which denotes the etf had a 0.2% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Fidelity Momentum Factor, which you can use to evaluate the volatility of the entity. Please confirm Fidelity Momentum's Downside Deviation of 0.8304, mean deviation of 0.6291, and Coefficient Of Variation of 438.34 to check if the risk estimate we provide is consistent with the expected return of 0.18%. The etf shows a Beta (market volatility) of 0.84, which means possible diversification benefits within a given portfolio. As returns on the market increase, Fidelity Momentum's returns are expected to increase less than the market. However, during the bear market, the loss of holding Fidelity Momentum is expected to be smaller as well.
Auto-correlation | -0.05 |
Very weak reverse predictability
Fidelity Momentum Factor has very weak reverse predictability. Overlapping area represents the amount of predictability between Fidelity Momentum time series from 19th of June 2024 to 17th of September 2024 and 17th of September 2024 to 16th 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 Fidelity Momentum Factor price movement. The serial correlation of -0.05 indicates that only as little as 5.0% of current Fidelity Momentum price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.05 | |
| Spearman Rank Test | -0.22 | |
| Residual Average | 0.0 | |
| Price Variance | 6.41 |
Fidelity Momentum Factor lagged returns against current returns
Autocorrelation, which is Fidelity Momentum 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 Fidelity Momentum's etf expected returns. We can calculate the autocorrelation of Fidelity Momentum returns to help us make a trade decision. For example, suppose you find that Fidelity Momentum 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 |
Fidelity Momentum 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 Momentum etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Fidelity Momentum etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Fidelity Momentum etf over time.
Current vs Lagged Prices |
| Timeline |
Fidelity Momentum Lagged Returns
When evaluating Fidelity Momentum's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Fidelity Momentum etf have on its future price. Fidelity Momentum 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 Momentum autocorrelation shows the relationship between Fidelity Momentum etf current value and its past values and can show if there is a momentum factor associated with investing in Fidelity Momentum Factor.
Regressed Prices |
| Timeline |
Pair Trading with Fidelity Momentum
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 Fidelity Momentum 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 Fidelity Momentum will appreciate offsetting losses from the drop in the long position's value.Moving together with Fidelity Etf
| 0.97 | VUG | Vanguard Growth Index Sell-off Trend | PairCorr |
| 0.97 | IWF | iShares Russell 1000 | PairCorr |
| 0.97 | IVW | iShares SP 500 | PairCorr |
| 0.97 | SPYG | SPDR Portfolio SP | PairCorr |
| 0.97 | IUSG | iShares Core SP | PairCorr |
Moving against Fidelity Etf
The ability to find closely correlated positions to Fidelity Momentum could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Fidelity Momentum 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 Fidelity Momentum - 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 Fidelity Momentum Factor to buy it.
The correlation of Fidelity Momentum 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 Fidelity Momentum moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Fidelity Momentum Factor 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 Fidelity Momentum 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 Fidelity Momentum Correlation, Fidelity Momentum Volatility and Fidelity Momentum Alpha and Beta module to complement your research on Fidelity Momentum. You can also try the Portfolio Anywhere module to track or share privately all of your investments from the convenience of any device.
Fidelity Momentum 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.