We study the statistics of the number of records R_{n,N} for N identical and
independent symmetric discrete-time random walks of n steps in one dimension,
all starting at the origin at step 0. At each time step, each walker jumps by a
random length drawn independently from a symmetric and continuous distribution.
We consider two cases: (I) when the variance \sigma^2 of the jump distribution
is finite and (II) when \sigma^2 is divergent as in the case of L\'evy flights
with index 0 < \mu < 2. In both cases we find that the mean record number
<R_{n,N}> grows universally as \sim \alpha_N \sqrt{n} for large n, but with a
very different behavior of the amplitude \alpha_N for N > 1 in the two cases.
We find that for large N, \alpha_N \approx 2 \sqrt{\log N} independently of
\sigma^2 in case I. In contrast, in case II, the amplitude approaches to an
N-independent constant for large N, \alpha_N \approx 4/\sqrt{\pi},
independently of 0<\mu<2. For finite \sigma^2 we argue, and this is confirmed
by our numerical simulations, that the full distribution of (R_{n,N}/\sqrt{n} -
2 \sqrt{\log N}) \sqrt{\log N} converges to a Gumbel law as n \to \infty and N
\to \infty. In case II, our numerical simulations indicate that the
distribution of R_{n,N}/\sqrt{n} converges, for n \to \infty and N \to \infty,
to a universal nontrivial distribution, independently of \mu. We discuss the
applications of our results to the study of the record statistics of 366 daily
stock prices from the Standard & Poors 500 index.