Emergence of statistically validated financial intraday lead-lag relationships

Abstract

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.

Keywords:

• Financial markets
• Stock returns
• High-frequency data
• Lead-lag correlations
• Bootstrap method
• Statistically validated networks

AMS Subject Classifications:

• C55
• C15
• G41

Acknowledgements

We thank Viktoria Dalko for useful conversations and insights, and her help with the data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional Information

The authors declare no competing financial interests.

Notes

All authors contributed equally to this manuscript.

Additional information

Funding

CC, DYK and HES wish to thank ONR [grant number N00014-09-1-0380], [grant number N00014-12-1-0548], DTRA [grant number HDTRA-1-10-1-0014], [grant number HDTRA-1-09-1-0035], and NSF [grant number CMMI 1125290]. M.T. and R.N.M. acknowledge support from the INET research project NetHet ‘New Tools in Credit Network Modeling with Heterogenous Agents’. R. N. M. acknowledge support from the FP7 research project CRISIS ‘Complexity Research Initiative for Systemic InstabilitieS’.