Toxic traders? The impact of active traders on institutional transaction costs Talis J. Putnins (UTS) CIFR Conference: Investment Management and Markets 24 May 2016

Acknowledgement and disclaimer This project is funded by CIFR and makes use of data from ASIC The data have been cleansed, aggregated and confidential details have been made anonymous The analysis expands on work being done within the ASIC surveillance department The views expressed here are those of the author and not necessarily those of ASIC or CIFR

Background Most of the academic literature concludes that AT/HFT have been a blessing to markets, making them more liquid and efficient, or at worst benign Yet many buy-side institutions claim they are a curse – Australian example: “as big institutional buyers and sellers, if we can’t find blocks we have to trade in smaller sizes, across multiple venues using algos... which leaves us open to being taken advantage of by HFT and other participants” … how can we reconcile these views?

Three possibilities Possibility 1: the academic literature does not measure the aspects of market quality that matter for institutional traders – e.g., spreads and depth vs actual transaction costs Possibility 2: AT/HFT studies might miss the culprits by focussing too narrowly – Predatory traders are not necessarily HFTs Possibility 3: there is considerable heterogeneity in the cross-section of AT/HFT and institutions may be concerned with a subset of them – A subset might systematically exploit some (unsophisticated?) institutions

This paper Objective: reconcile the apparent disagreement between institutions/studies a)To what extent do some traders systematically increase institutional execution costs (‘toxic traders’)? b)Are the effects of toxic traders offset by other traders? c)What characteristics distinguish toxic traders from others? Address the three potential sources of disagreement: 1)Accurate measurement of institutional transaction costs by reconstructing large parent orders from regulatory data, 2)Cast a broader net across traders than AT/HFT studies— examine all high-turnover non-directional traders (includes, but is not limited to, HFT) 3)Characterise the heterogeneity in how active traders impact institutional transaction costs

Data ASX 200 Index constituents 13 month period Sept 2014 to Oct 2015 At the stock-day level: 52,873 observations Use ASIC surveillance and regulatory data ("origin of order" identifier) to: – Identify active non-directional traders and measure their presence; and – Identify large unidirectional traders (‘investors’) and measure their transaction costs ASIC processes all data containing individual identifiers. All individual identifiers and security codes are removed. – Institutional data is aggregated on a stock-day basis. – Trader data is presented as aggregate trader-stock-day turnover weightings.

The approach – Step 1 Reconstruct institutional unidirectional trade packages: a)Aggregate all individual transactions for an identified user/account within a stock-day into a single parent order, b)Classify the parent order as unidirectional if all trades are in one direction (buying or selling), and c)Classify the unidirectional parent orders as institutional if its $volume exceeds the median of all unidirectional parent orders that stock-day and the parent order is “worked” for at least 4 hours

The approach – Step 2

Large institution orders as % of turnover Note: Simple averages across stocks within quartiles

Institution execution costs through time Average implementation shortfall (bps) Note: Simple averages across stocks within quartiles

The approach – Step 3

Active trader participation through time Share of turnover (%)

The approach – Step 4

The TOXICITY FRONTIER

Refinements of the basic toxicity regression

The approach – Step 5

IV quality 1.Strong? Yes. – Correlation with endogenous variable: 0.54 to 0.62 (depending on the lag) – F-stats: 1,923,893, significant at 1% 2.Exogenous + exclusion restriction? – Satisfied through temporal offset: previous activity cannot be caused by current conditions (except for persistence) Address persistence with (i) control for lagged IS, (ii) time FE, (iii) omit first two lags and use 3 rd, 4 th, 5 th.

Correlations with OLS estimates: 0.70 (0.48)

The approach – Step 6 Even if active traders had no effect on IS, with enough of them, some will by chance appear to have a significant effect – Data mining bias – Type 1 errors become highly likely with repeat tests Further complication: need to account for dependencies in the data and non-normality of the distribution of toxicity – Similar problem as that faced in gauging fund manager skill - with many managers (even with no skill) some will by chance repeatedly outperform the benchmark

Solution: bootstrap

Bootstrap results Conclusions: – The 5% most toxic, are considerably more toxic than would occur by chance (<1% chance of getting these toxicity levels by statistical chance) – Also the 5% most beneficial are beneficial beyond chance (<1% chance of a fluke)

Bootstrap conclusions By statistical chance alone, of the 187 active traders, we would expect – 5.6 to be statistically toxic at 95% confidence (t-stat>2) – 1.2 to be statistically toxic at 99.5% confidence (t-stat>3) In reality, of the 187 active traders, – 12 are statistically toxic at 95% confidence (t-stat>2) The prob of getting that many by chance is <1% – 4 are statistically toxic at 99.5% confidence (t-stat>3) The prob of getting that many by chance is <2% Similarly, by chance we should see – 6.9 to be statistically beneficial at 95% conf (t-stat<-2) – 1.2 to be statistically beneficial at 99.5% conf (t-stat<-3) In reality, of the 187 active traders, – 15 are statistically beneficial at 95% conf (t-stat<-2) The prob of getting that many by chance is <1% – 4 are statistically beneficial at 99.5% conf (t-stat<-3) The prob of getting that many by chance is <2%

Net toxicity? Economic significance?

BUT, the cost of toxicity should not be taken in isolation, because: The 15 significantly beneficial traders as a group: – Reduce implementation shortfall by 9 bps, saving around $375 million in institutional execution costs The net effects across all active traders is near zero (+/- 1 bp, and not statistically different from zero) Consistent with other evidence that HFT as a group are benign (including ASIC, 2015), and Explains why there are concerns about toxic traders: their effects in isolation are considerable!

Characteristics of toxic traders

Conclusions Developed a methodology to identify the causal impact of different traders on institutional transaction costs, accounting for (i) endogeneity, and (ii) chance/data mining bias Considerable heterogeneity across active traders: – Toxic traders add 10 bps to the cost of executing large orders – Beneficial traders offset the effect of toxic traders, such that in aggregate active traders are benign Characteristics of active traders: – HFT not more toxic than others – More toxic trading in smaller stocks Policy implications / industry implications

Thank you

Appendix

Types of traders