Perhaps the most simple approach may be to measure the difference of all 163 share prices before and after the event (say a week before and a week after). You can normalize them so you will have relative change before and after as percentage of before.
Out of this, 50 163-dimensional vectors will be available $x_{1},\dots x_{50}$ with components $x_{1,1}\dots x_{50,163}$. These may be aggregated with respect to what you understand as the importance, e.g:
- Max average relative change - what increased the prices most
- Min average relative change - what decreased the prices most
- Max average absolute relative change - what changed the prices most
- Span of relative changes - what caused most changes in dynamics among the companies $$\max_{i=1\dots50} \left(\max_{j=1\dots 163 }x_{i,j}-\min_{j=1\dots 163 }x_{i,j}\right)$$