Target network is not more stable. Both networks are the same in the regard that no one is more stable than the other. The reason for using a target network is that your current network after each step is updated. So, by not using a target network and using just the current network, after each update the rewards for many states will be modified slightly. So, for a particular state, after each update, the reward will be modified which will lead to an unstable reward. This happens because when you update Q value function for state S, you may also slightly change the reward it predicts for state S' in the next step. So, each update to Q value function changes slightly the rewards for many other states, which makes the predicted rewards in all those states to be slightly unstable, as they are changed very often and you dont have a clear reward in those states.
However, by using a target network, your reward will be more stable(as you dont update your target network at each step, but you only update the current network).
So, by using a target network, the update is more stable, not the individual networks. And because the target network is an older version of the current network, it is sensible to use the current network as it was trained more than the target network