Fidelity Emerging Markets Fund Probability of Future Fund Price Finishing Under 8.78
| FCEM Fund | 10.10 0.08 0.79% |
Fidelity |
Fidelity Emerging Target Price Odds to finish below 8.78
The tendency of Fidelity Fund price to converge on an average value over time is a known aspect in finance that investors have used since the beginning of the stock market for forecasting. However, many studies suggest that some traded equity instruments are consistently mispriced before traders' demand and supply correct the spread. One possible conclusion to this anomaly is that these stocks have additional risk, for which investors demand compensation in the form of extra returns.
| Current Price | Horizon | Target Price | Odds to drop to 8.78 or more in 90 days |
| 10.10 | 90 days | 8.78 | near 1 |
Based on a normal probability distribution, the odds of Fidelity Emerging to drop to 8.78 or more in 90 days from now is near 1 (This Fidelity Emerging Markets probability density function shows the probability of Fidelity Fund to fall within a particular range of prices over 90 days) . Probability of Fidelity Emerging Markets price to stay between 8.78 and its current price of 10.1 at the end of the 90-day period is about 33.74 .
Assuming the 90 days trading horizon Fidelity Emerging has a beta of 0.28. This usually indicates as returns on the market go up, Fidelity Emerging average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding Fidelity Emerging Markets will be expected to be much smaller as well. Additionally Fidelity Emerging Markets has an alpha of 0.0277, implying that it can generate a 0.0277 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta). Fidelity Emerging Price Density |
| Price |
Predictive Modules for Fidelity Emerging
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Fidelity Emerging Markets. Regardless of method or technology, however, to accurately forecast the fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the fund market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.Fidelity Emerging Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. Fidelity Emerging is not an exception. The market had few large corrections towards the Fidelity Emerging's value, including both sudden drops in prices as well as massive rallies. These swings have made and broken many portfolios. An investor can limit the violent swings in their portfolio by implementing a hedging strategy designed to limit downside losses. If you hold Fidelity Emerging Markets, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of Fidelity Emerging within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | 0.03 | |
β | Beta against Dow Jones | 0.28 | |
σ | Overall volatility | 0.43 | |
Ir | Information ratio | -0.05 |
Fidelity Emerging Technical Analysis
Fidelity Emerging's future price can be derived by breaking down and analyzing its technical indicators over time. Fidelity Fund technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Fidelity Emerging Markets. In general, you should focus on analyzing Fidelity Fund price patterns and their correlations with different microeconomic environments and drivers.
Fidelity Emerging Predictive Forecast Models
Fidelity Emerging's time-series forecasting models is one of many Fidelity Emerging's fund analysis techniques aimed to predict future share value based on previously observed values. Time-series forecasting models are widely used for non-stationary data. Non-stationary data are called the data whose statistical properties, e.g., the mean and standard deviation, are not constant over time, but instead, these metrics vary over time. This non-stationary Fidelity Emerging's historical data is usually called time series. Some empirical experimentation suggests that the statistical forecasting models outperform the models based exclusively on fundamental analysis to predict the direction of the fund market movement and maximize returns from investment trading.
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 Emerging 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 Emerging's short interest history, or implied volatility extrapolated from Fidelity Emerging options trading.
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