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. Furthermore, 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.

Price response functions and spread impact in correlated financial markets

“…average (25) where the index i and j correspond to stocks in the market, r (scale) i is the return of the stock i in a time lag τ with a time shift t s in the corresponding scale and ε (scale) j is the trade sign of the stock j in the corresponding scale. The superscript scale refers to the time scale used, whether physical time scale (scale = p) or trade We compute the response functions according to two cases.…”

“…To facilitate the reproduction of our results, the source code for the data analysis is available in Ref. [25].…”