
Recent empirical studies have demonstrated long-memory in the signs of orders to buy or sell in financial markets [2, 19]. We show how this can be caused by delays in market clearing. Under the common practice of order splitting, large orders are broken up into pieces and executed incrementally. If the size of such large orders is power law distributed, this gives rise to power law decaying autocorrelations in the signs of executed orders. More specifically, we show that if the cumulative distribution of large orders of volume v is proportional to v to the power -alpha and the size of executed orders is constant, the autocorrelation of order signs as a function of the lag tau is asymptotically proportional to tau to the power -(alpha - 1). This is a long-memory process when alpha < 2. With a few caveats, this gives a good match to the data. A version of the model also shows long-memory fluctuations in order execution rates, which may be relevant for explaining the long-memory of price diffusion rates.
12 pages, 7 figures
Physics - Physics and Society, Quantitative Finance - Trading and Market Microstructure, Commerce, FOS: Physical sciences, Physics and Society (physics.soc-ph), Trading and Market Microstructure (q-fin.TR), Condensed Matter - Other Condensed Matter, FOS: Economics and business, Execution, Commerce, optimal liquidation, Execution, optimal liquidation, Other Condensed Matter (cond-mat.other)
Physics - Physics and Society, Quantitative Finance - Trading and Market Microstructure, Commerce, FOS: Physical sciences, Physics and Society (physics.soc-ph), Trading and Market Microstructure (q-fin.TR), Condensed Matter - Other Condensed Matter, FOS: Economics and business, Execution, Commerce, optimal liquidation, Execution, optimal liquidation, Other Condensed Matter (cond-mat.other)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 69 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
