SF Sustainable (Switzerland) Probability of Future Fund Price Finishing Under 120.34
| SFPF Fund | 131.50 2.00 1.54% |
SFPF |
SF Sustainable Target Price Odds to finish below 120.34
The tendency of SFPF 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 120.34 or more in 90 days |
| 131.50 | 90 days | 120.34 | near 1 |
Based on a normal probability distribution, the odds of SF Sustainable to drop to 120.34 or more in 90 days from now is near 1 (This SF Sustainable Property probability density function shows the probability of SFPF Fund to fall within a particular range of prices over 90 days) . Probability of SF Sustainable Property price to stay between 120.34 and its current price of 131.5 at the end of the 90-day period is close to 99 .
Assuming the 90 days trading horizon SF Sustainable Property has a beta of -0.0229. This usually implies as returns on the benchmark increase, returns on holding SF Sustainable are expected to decrease at a much lower rate. During a bear market, however, SF Sustainable Property is likely to outperform the market. Additionally SF Sustainable Property has an alpha of 0.0662, implying that it can generate a 0.0662 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta). SF Sustainable Price Density |
| Price |
Predictive Modules for SF Sustainable
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as SF Sustainable Property. 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.SF Sustainable Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. SF Sustainable is not an exception. The market had few large corrections towards the SF Sustainable'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 SF Sustainable Property, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of SF Sustainable within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | 0.07 | |
β | Beta against Dow Jones | -0.02 | |
σ | Overall volatility | 1.52 | |
Ir | Information ratio | 0.03 |
SF Sustainable Technical Analysis
SF Sustainable's future price can be derived by breaking down and analyzing its technical indicators over time. SFPF Fund technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of SF Sustainable Property. In general, you should focus on analyzing SFPF Fund price patterns and their correlations with different microeconomic environments and drivers.
SF Sustainable Predictive Forecast Models
SF Sustainable's time-series forecasting models is one of many SF Sustainable'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 SF Sustainable'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 SF Sustainable 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, SF Sustainable's short interest history, or implied volatility extrapolated from SF Sustainable options trading.
Other Information on Investing in SFPF Fund
SF Sustainable financial ratios help investors to determine whether SFPF 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 SFPF with respect to the benefits of owning SF Sustainable security.
| Share Portfolio Track or share privately all of your investments from the convenience of any device | |
| Portfolio Diagnostics Use generated alerts and portfolio events aggregator to diagnose current holdings | |
| Price Transformation Use Price Transformation models to analyze the depth of different equity instruments across global markets |