College Retirement Equities Fund Probability of Future Fund Price Finishing Over 523.63
| QCEQRX Fund | USD 520.94 3.06 0.58% |
College |
College Retirement Target Price Odds to finish over 523.63
The tendency of College 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 move over $ 523.63 or more in 90 days |
| 520.94 | 90 days | 523.63 | about 5.35 |
Based on a normal probability distribution, the odds of College Retirement to move over $ 523.63 or more in 90 days from now is about 5.35 (This College Retirement Equities probability density function shows the probability of College Fund to fall within a particular range of prices over 90 days) . Probability of College Retirement price to stay between its current price of $ 520.94 and $ 523.63 at the end of the 90-day period is roughly 2.34 .
Assuming the 90 days trading horizon College Retirement has a beta of 0.81 indicating as returns on the market go up, College Retirement average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding College Retirement Equities will be expected to be much smaller as well. Additionally College Retirement Equities has an alpha of 0.0709, implying that it can generate a 0.0709 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta). College Retirement Price Density |
| Price |
Predictive Modules for College Retirement
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as College Retirement. 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.College Retirement Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. College Retirement is not an exception. The market had few large corrections towards the College Retirement'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 College Retirement Equities, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of College Retirement within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | 0.07 | |
β | Beta against Dow Jones | 0.81 | |
σ | Overall volatility | 14.49 | |
Ir | Information ratio | 0.07 |
College Retirement Technical Analysis
College Retirement's future price can be derived by breaking down and analyzing its technical indicators over time. College Fund technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of College Retirement Equities. In general, you should focus on analyzing College Fund price patterns and their correlations with different microeconomic environments and drivers.
College Retirement Predictive Forecast Models
College Retirement's time-series forecasting models is one of many College Retirement'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 College Retirement'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 College Retirement 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, College Retirement's short interest history, or implied volatility extrapolated from College Retirement options trading.
Other Information on Investing in College Fund
College Retirement financial ratios help investors to determine whether College Fund is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in College with respect to the benefits of owning College Retirement security.
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