20,458 research outputs found
Order flow dynamics around extreme price changes on an emerging stock market
We study the dynamics of order flows around large intraday price changes
using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a
significant reversal of price for both intraday price decreases and increases
with a permanent price impact. The volatility, the volume of different types of
orders, the bid-ask spread, and the volume imbalance increase before the
extreme events and decay slowly as a power law, which forms a well-established
peak. The volume of buy market orders increases faster and the corresponding
peak appears earlier than for sell market orders around positive events, while
the volume peak of sell market orders leads buy market orders in the magnitude
and time around negative events. When orders are divided into four groups
according to their aggressiveness, we find that the behaviors of order volume
and order number are similar, except for buy limit orders and canceled orders
that the peak of order number postpones two minutes later after the peak of
order volume, implying that investors placing large orders are more informed
and play a central role in large price fluctuations. We also study the relative
rates of different types of orders and find differences in the dynamics of
relative rates between buy orders and sell orders and between individual
investors and institutional investors. There is evidence showing that
institutions behave very differently from individuals and that they have more
aggressive strategies. Combing these findings, we conclude that institutional
investors are more informed and play a more influential role in driving large
price fluctuations.Comment: 22 page
Preferred numbers and the distribution of trade sizes and trading volumes in the Chinese stock market
The distribution of trade sizes and trading volumes are investigated based on
the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen
Stock Exchange in the whole year 2003. We observe that the size distribution of
trades for individual stocks exhibits jumps, which is caused by the number
preference of traders when placing orders. We analyze the applicability of the
"-Gamma" function for fitting the distribution by the Cram\'{e}r-von Mises
criterion. The empirical PDFs of trading volumes at different timescales
ranging from 1 min to 240 min can be well modeled. The
applicability of the -Gamma functions for multiple trades is restricted to
the transaction numbers . We find that all the PDFs have
power-law tails for large volumes. Using careful estimation of the average tail
exponents of the distribution of trade sizes and trading volumes, we
get , well outside the L{\'e}vy regime.Comment: 7 pages, 5 figures and 4 table
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