This really depends on your methods. Earlier today in a different question I talked about confidence intervals using a very simplistic Gaussian model. I could reproduce that example to fit with your example:
- Select some time lag for your data.
- Calculate the rate of returns for each time step.
- Calculate the standard deviation and mean of the rate of returns. Say the standard deviation is $5.1\%$ and the mean is $5.0\%$.
- Choose a confidence interval, say, $95\%$.
- Use the inverse of the distribution for the interval $2.5\%$ and $97.5\%$ (a width of $95$ percentage points). For the Gaussian distribution this gives z-values of $-1.96$ and $+1.96$.
- Calculate the interval, which comes out to be $-5\% = (5\% - 1.96 \times 5.1\%)$ and $+15\% = (5\% + 1.96 \times 5.1\%)$.
- If the price of the asset is $\$100$ then your confidence interval would be between $\$95 = \$100 \times (1-0.05)$ and $\$115 = \$100 \times (1+0.15)$ with a confidence of $95\%$, meaning that you typically wouldn't see the price go over or under this interval more often than once every $20$ days $(=1/0.05)$.
Notice that this conforms to your statement that "Most of the times the target is achieved", in fact 50% of the time it would've reached the target, but only because I chose a standard deviation and mean which fit your example data. If the analyst did these calculations (with better assumptions I hope) then they could make statements that say something like:
The current market price is \$100. The expected/target return tomorrow is \$105, meaning that half of the time, in circumstances like this, we'd see the price go over this target. That said, one in twenty times we'd expect to see it fall below \$95, thus we set our stop loss there. Small print: Mean return 5%; standard deviation 5.1%; Gaussian model.
Normally you'd hope the analyst used something better than a simple Gaussian model.