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. 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

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 $$\max_{i=1\dots50} \left(\max_{j=1\dots 163 }x_{i,j}-\min_{j=1\dots 163 }x_{i,j}\right)$$