Deviations in expected price impact for small transaction volumes under fee restructuring

Abstract

We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve after fee reductions, it is found that the price impact corresponding to smaller volume trades is greater than expected relative to prior estimates for a range of listed stocks. We show that a master curve for price impact can be found following rescaling by an appropriate liquidity proxy, providing a means for practitioners to approximate price impact curves without onerous processing of tick data.

Applications of Machine Learning to Estimating the Sizes and Market Impact of Hidden Orders in the BRICS Financial Markets

“…To attain our first objective we calculated the average daily value traded per firm, as per Harvey et al (2016), which we applied as market capitalization proxy of each firm in each market. The periodic market capitalizations of firms that are reported at the closing dates of the financial periods of firms would be a loose approach to judging the performance of firms when we consider the possibility that firm ranks may change in real time.…”

“…Approach : Lillo et al (2003) & Harvey et al (2016 the average market impact curves. Market capitalization does not change the structure of the average market impact curves; however, it distinguishes firms that are costly to trade and firms that are less costly to trade.…”

“…Fellow researchers such as Harvey et al (2016) argued that perhaps if the Lillo et al (2003) was to be implemented in a different market with small changes on the choice of parameters, then different results could be observed. Based on the research of Lillo et al (2003) and Harvey et al (2016), we argue diligently that if hidden orders were to be accounted for on the calculations of average market impact curves, then different structures of the average market impact curves could emerge.…”

“…We acknowledge the contributions of Lillo et al (2003) and Harvey et al (2016) for providing feasible research that allows our research to be formulated. We proceed to investigate the micro-structures of five BRICS financial markets.…”

Can agent-based models probe market microstructure?

“…This phenomenon can be observed by plotting the autocorrelation function of the time series of trade signs for a particular market over some period (+1 for buyer initiated trades and -1 for seller initiated trades), which typically demonstrates a slow decay in autocorrelation as the number of lags is increased in most empirical investigations (Kuroda et al 2011). Using the measured data and following the method of Lee and Ready (1991), which was also applied to similar datasets in Harvey et al (2017), we classified trades as either buyer or seller initiated and computed the associated trade sign autocorrelation function. This autocorrelation function is presented in Figure 6 and confirms that long memory dynamics are captured in the order flow associated with the data to which the model was calibrated.…”