This is a (maybe unexpected) explanation using the so-called quadratic Bezier representation of parabolas.
(classical) result: Any parabola can be given the following parametric representation:
$$\binom{x}{y}=(1-t)^2\binom{x_A}{y_A}+2t(1-t)\binom{x_B}{y_B}+t^2\binom{x_C}{y_C}.$$
(Convert segment of parabola to quadratic bezier curve)
(https://en.wikipedia.org/wiki/B%C3%A9zier_curve)
Symbolically:
$$\tag{1} M=(1-t)^2 A+2t(1-t)B+t^2 C.$$
where $A$ and $C$ are any distinct points of the parabola, and intermediate point $B$ is any point not aligned with $A$ and $B$ (in fact, it is the point of intersection of tangents in $A$ and $C$). Let us denote this parametric curve by ${\frak B}(A,B,C)$ : it is the Bezier curve defined by points $A,B,C$.
Now, applying any (invertible) linear transformation $L$ to (1) gives:
$$\tag{2} L(M)=(1-t)^2 L(A)+2t(1-t)L(B)+t^2 L(C),$$
(2) means that the image of Bezier curve ${\frak B}(A,B,C)$ by linear transformation $L$ is Bezier curve ${\frak B}(L(A),L(B),L(C))$, thus a parabola.
Remarks:
1) I leave as an exercise the fact that $L(A),L(B),L(C)$ aren't aligned (the fact that $L$ is invertible play here a role).
2) The sum of the weights in relationship (1) is always 1:
$$\forall t \in \mathbb{R} \ \ \ \ \ (1-t)^2 +2t(1-t)+t^2=1 $$
3) More generally, a linear transform preserves weighted sums (barycenters): the image of a weighted sum of vectors is the weighted sum of their images (with the same coefficients).