  According to the leading models in modern finance, the presence of intraday
lead-lag relationships between financial assets is negligible in efficient
markets. With the advance of technology, however, markets have become more
sophisticated. To determine whether this has resulted in an improved market
efficiency, we investigate whether statistically significant lagged correlation
relationships exist in financial markets. We introduce a numerical method to
statistically validate links in correlation-based networks, and employ our
method to study lagged correlation networks of equity returns in financial
markets. Crucially, our statistical validation of lead-lag relationships
accounts for multiple hypothesis testing over all stock pairs. In an analysis
of intraday transaction data from the periods 2002--2003 and 2011--2012, we
find a striking growth in the networks as we increase the frequency with which
we sample returns. We compute how the number of validated links and the
magnitude of correlations change with increasing sampling frequency, and
compare the results between the two data sets. Finally, we compare topological
properties of the directed correlation-based networks from the two periods
using the in-degree and out-degree distributions and an analysis of three-node
motifs. Our analysis suggests a growth in both the efficiency and instability
of financial markets over the past decade.
