SBI Mutual (India) Probability of Future Etf Price Finishing Over 242.31
| SETF10GILT | 242.86 0.31 0.13% |
SBI |
SBI Mutual Target Price Odds to finish over 242.31
The tendency of SBI Etf 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 242.31 in 90 days |
| 242.86 | 90 days | 242.31 | about 5.23 |
Based on a normal probability distribution, the odds of SBI Mutual to stay above 242.31 in 90 days from now is about 5.23 (This SBI Mutual Fund probability density function shows the probability of SBI Etf to fall within a particular range of prices over 90 days) . Probability of SBI Mutual Fund price to stay between 242.31 and its current price of 242.86 at the end of the 90-day period is under 4.
Assuming the 90 days trading horizon SBI Mutual has a beta of 0.0656. This usually implies as returns on the market go up, SBI Mutual average returns are expected to increase less than the benchmark. However, during the bear market, the loss on holding SBI Mutual Fund will be expected to be much smaller as well. Additionally SBI Mutual Fund has an alpha of 0.0181, implying that it can generate a 0.0181 percent excess return over Dow Jones Industrial after adjusting for the inherited market risk (beta). SBI Mutual Price Density |
| Price |
Predictive Modules for SBI Mutual
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as SBI Mutual Fund. Regardless of method or technology, however, to accurately forecast the etf market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the etf 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.SBI Mutual Risk Indicators
For the most part, the last 10-20 years have been a very volatile time for the stock market. SBI Mutual is not an exception. The market had few large corrections towards the SBI Mutual'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 SBI Mutual Fund, one way to have your portfolio be protected is to always look up for changing volatility and market elasticity of SBI Mutual within the framework of very fundamental risk indicators.α | Alpha over Dow Jones | 0.02 | |
β | Beta against Dow Jones | 0.07 | |
σ | Overall volatility | 1.30 | |
Ir | Information ratio | -0.32 |
SBI Mutual Technical Analysis
SBI Mutual's future price can be derived by breaking down and analyzing its technical indicators over time. SBI Etf technical analysis helps investors analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of SBI Mutual Fund. In general, you should focus on analyzing SBI Etf price patterns and their correlations with different microeconomic environments and drivers.
SBI Mutual Predictive Forecast Models
SBI Mutual's time-series forecasting models is one of many SBI Mutual's etf 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 SBI Mutual'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 etf 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 SBI Mutual 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, SBI Mutual's short interest history, or implied volatility extrapolated from SBI Mutual options trading.