The Gabelli Focus Fund Probability of Future Mutual Fund Price Finishing Over 16.40
| GWSAX Fund | USD 17.95 0.13 0.73% |
Gabelli |
Gabelli Focus Target Price Odds to finish over 16.40
The tendency of Gabelli 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 stay above $ 16.40 in 90 days |
| 17.95 | 90 days | 16.40 | close to 99 |
Based on a normal probability distribution, the odds of Gabelli Focus to stay above $ 16.40 in 90 days from now is close to 99 (This The Gabelli Focus probability density function shows the probability of Gabelli Mutual Fund to fall within a particular range of prices over 90 days) . Probability of Gabelli Focus price to stay between $ 16.40 and its current price of $17.95 at the end of the 90-day period is roughly 97.0 .
Assuming the 90 days horizon Gabelli Focus has a beta of 0.69. This usually indicates as returns on the market go up, Gabelli Focus average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding The Gabelli Focus will be expected to be much smaller as well. Additionally The Gabelli Focus has an alpha of 0.0218, implying that it can generate a 0.0218 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta). Gabelli Focus Price Density |
| Price |
Predictive Modules for Gabelli Focus
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Gabelli Focus. 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.Gabelli Focus Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. Gabelli Focus is not an exception. The market had few large corrections towards the Gabelli Focus' 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 The Gabelli Focus, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of Gabelli Focus within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | 0.02 | |
β | Beta against Dow Jones | 0.69 | |
σ | Overall volatility | 0.32 | |
Ir | Information ratio | -0.02 |
Gabelli Focus Technical Analysis
Gabelli Focus' future price can be derived by breaking down and analyzing its technical indicators over time. Gabelli Mutual Fund technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of The Gabelli Focus. In general, you should focus on analyzing Gabelli Mutual Fund price patterns and their correlations with different microeconomic environments and drivers.
Gabelli Focus Predictive Forecast Models
Gabelli Focus' time-series forecasting models is one of many Gabelli Focus' 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 Gabelli Focus' 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 Gabelli Focus 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, Gabelli Focus' short interest history, or implied volatility extrapolated from Gabelli Focus options trading.
Other Information on Investing in Gabelli Mutual Fund
Gabelli Focus financial ratios help investors to determine whether Gabelli 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 Gabelli with respect to the benefits of owning Gabelli Focus security.
| Share Portfolio Track or share privately all of your investments from the convenience of any device | |
| My Watchlist Analysis Analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like | |
| Analyst Advice Analyst recommendations and target price estimates broken down by several categories | |
| Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm |