Oklahoma College Savings Fund Probability of Future Mutual Fund Price Finishing Under 13.56
| FMOKX Fund | USD 13.60 0.04 0.29% |
Oklahoma |
Oklahoma College Target Price Odds to finish below 13.56
The tendency of Oklahoma Mutual 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 $ 13.56 or more in 90 days |
| 13.60 | 90 days | 13.56 | about 54.38 |
Based on a normal probability distribution, the odds of Oklahoma College to drop to $ 13.56 or more in 90 days from now is about 54.38 (This Oklahoma College Savings probability density function shows the probability of Oklahoma Mutual Fund to fall within a particular range of prices over 90 days) . Probability of Oklahoma College Savings price to stay between $ 13.56 and its current price of $13.6 at the end of the 90-day period is about 10.24 .
Assuming the 90 days horizon Oklahoma College has a beta of 0.51. This usually indicates as returns on the market go up, Oklahoma College average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding Oklahoma College Savings will be expected to be much smaller as well. Additionally Oklahoma College Savings has a negative alpha, implying that the risk taken by holding this instrument is not justified. The company is significantly underperforming the Dow Jones Industrial. Oklahoma College Price Density |
| Price |
Predictive Modules for Oklahoma College
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Oklahoma College Savings. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual 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.Oklahoma College Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. Oklahoma College is not an exception. The market had few large corrections towards the Oklahoma College'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 Oklahoma College Savings, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of Oklahoma College within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | -0.04 | |
β | Beta against Dow Jones | 0.51 | |
σ | Overall volatility | 0.15 | |
Ir | Information ratio | -0.18 |
Oklahoma College Technical Analysis
Oklahoma College's future price can be derived by breaking down and analyzing its technical indicators over time. Oklahoma Mutual Fund technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Oklahoma College Savings. In general, you should focus on analyzing Oklahoma Mutual Fund price patterns and their correlations with different microeconomic environments and drivers.
Oklahoma College Predictive Forecast Models
Oklahoma College's time-series forecasting models is one of many Oklahoma College's mutual 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 Oklahoma College'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 mutual 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 Oklahoma College 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, Oklahoma College's short interest history, or implied volatility extrapolated from Oklahoma College options trading.
Other Information on Investing in Oklahoma Mutual Fund
Oklahoma College financial ratios help investors to determine whether Oklahoma Mutual 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 Oklahoma with respect to the benefits of owning Oklahoma College security.
| Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm | |
| Funds Screener Find actively-traded funds from around the world traded on over 30 global exchanges | |
| Share Portfolio Track or share privately all of your investments from the convenience of any device |