Regularities and irregularities in order flow data

Abstract

We identify and analyze statistical regularities and irregularities in the recent order flow of different NASDAQ stocks, focusing on the positions where orders are placed in the orderbook. This includes limit orders being placed outside of the spread, inside the spread and (effective) market orders. We find that limit order placement inside the spread is strongly determined by the dynamics of the spread size. Most orders, however, arrive outside of the spread. While for some stocks order placement on or next to the quotes is dominating, deeper price levels are more important for other stocks. As market orders are usually adjusted to the quote volume, the impact of market orders depends on the orderbook structure, which we find to be quite diverse among the analyzed stocks as a result of the way limit order placement takes place.

Price response functions and spread impact in correlated financial markets

“…Thus, even if the responses functions are in the same scale, their values differ from one to another. We choose the spread [36] to group 524 stocks in the NAS-DAQ stock market for the year 2008 in physical time scale, and check how the average strength of the price self-response functions in physical time scale behaved for this groups. For each stock, we compute the spread in every second along the market time.…”

Two Price Regimes in Limit Order Books: Liquidity Cushion and Fragmented Distant Field

“…It is an important measure for the market liquidity, a small spread implies a low cost for trading. Moreover, the overall structure of the orderbook is found to be highly dependent on the size of the spread [18]. Stocks with a spread as small as one tick are characterized as large-tick stocks with a densely filled order book around the quotes and a sparsely filled order book far from the quotes.…”

“…In this context an improvement of agent based models based on the analysis of order flow dynamics is desirable [11][12][13][14]. While stylized facts are well-established for price time series [15,16], this is not yet the case for order book dynamics [17,18]. The distribution of liquidity within the limit order book is crucial for the stability of the limit order book and the response to large trades [13].…”

Price response functions and spread impact in correlated financial markets

“…Thus, even if the responses functions are in the same scale, their values differ from one to another. We choose the spread [35] to group 524 stocks in the NASDAQ stock market for the year 2008 in physical time scale, and check how the average strength of the price self-response functions in physical time scale behaved for this groups. For each stock we compute the spread in every second along We used three intervals to select the stocks groups (s < 0.05$, 0.05$ ≤ s < 0.10$ and 0.10$ ≤ s < 0.40$).…”