Price response functions and spread impact in correlated financial markets

Abstract

Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.

Comparing the market microstructure between two South African exchanges

“…The price response of a trade has long been central to the study of market microstructure [8,18,22,31]. Price response, or specifically market impact, continues to have practical interest because of the centrality it holds within the Almgren and Chriss framework [4] for managing the cost of trading.…”

“…Hence, our focus here relates to visualising price response in two very different exchanges. Nonetheless, we are mindful of the extent of the work carried out in mature markets [6,22,42,43].…”

“…Average cross-responses for a given stock are evaluated either with respect to the whole market or to different sectors. Related to all these, Henao-Londono et al [22] provides a comparison of different measures of price response and how they modify the results. More specifically, they compare responses calculated on the trade time scale and the physical time scale by making the assumption that midprices in the trade time scale are the same as that in the physical time scale.…”