LEARNING FROM THE FLASH-CRASH.

Scott Burrill, CFA, Partner and Managing Director, and Xiang Li, PhD, Director and Head of Quantitative Research at Rosenblatt Securities Inc, argue the case for the effectiveness of a volume based TCA framework.

Living and driving in heavily populated Southern California has made us appreciate our smartphone’s map and traffic applications. Being able to quickly navigate where we are headed, as well as avoid congested routes and find better alternatives greatly enhance the travels to our destination. In that same vein, traditional TCA tools and outmoded modelling techniques have failed to provide the trader with the ideal route in their quest for best execution or quickly navigate the changing market environment.

Many TCA tools have been developed since MiFID and MiFID II, but few of them take live “market theme” (i.e., a selling market or buying market) into consideration. The fragmentation of venues, the heavily “quant” oriented HFT and algo players, and the attendant need to avoid information leakage, all call for a comprehensive suite of tools which can readjust constantly to live market conditions, detect adverse selection, and help traders make informed decisions. Volume based TCA solutions shed light on such solutions.

This framework was initially developed in the study of the proverbial US Flash Crash to detect toxic trade flows and monitor a healthy trading environment live. Instead of looking at the market in traditional clock time, the new approach separates volume into equal sized bins and works on volume grids (we call it the “volume time”). By differentiating the possible informed buying money flow from the selling money flow, an adaptive model can create a reliable gauge of the current market regime of probable informed trades. The Volume Synchronised Probability of INformed Trading, aka VPIN, is one such pervasively studied measure and has proven to be a strong predictor of liquidity induced volatility, which is one major factor of trading costs.

Information is king

As trading pros know, price and volume information is highly informative. Trading volume is considered one of the most widely used factors in price prediction. The variation and interaction between price and volume not only provides information regarding the rate of information flow into the marketplace, but also reveals whether investors’ interpretations of the information are consistent or differing. VPIN, as an advanced mathematical model, was initially used to measure order flow toxicity. The model received wide attention in its ability to anticipate the US “Flash Crash” on May 6, 2010, more than one hour in advance. After that, researchers continued to study its application to futures trading and modelling market microstructures. We developed our dynamic, adaptive pre trade trading system based on VPIN called “SNAP” (Second Nature Analytics for Pretrade) as a next generation tool to help traders level the playing field in their quest to achieve best execution.

The benefits of adopting the Volume based TCA system are threefold.

• First, it provides transparency on probable informed order flow versus uninformed order flow. With the Volume based TCA framework, you can not only tell whether there is potentially toxic order flow in the underlying trading activity, but also have a clear mind on whether the probable informed orders are on the buy side or the sell side. From this information, the system can provide an optimised balance of sourcing liquidity and hiding your footprint in the market.

• Second, volume-based analysis enables comparison between liquid equities with illiquid equities since the volume bins will be based on uniformly distributed liquidity instead of time. An illiquid equity may have interrupted volume with a flat price line in the time space. But it would have continuous volume and price variations in the volume time space.

• Third, working in a volume-time space captures the dimensionality of the market environment more clearly. As Robert Almgren succinctly stated in his 2005 paper, “The level of market activity is known to vary substantially and consistently between different periods of the trading day… this intraday variation affects both the volume profile and the variance of prices.” By looking at variations in the volume time domain, the smile curve of volume becomes a uniform flat line. Many measurements, such as volatility and associated trading risk, enable better statistical characteristics with the uniformly distributed volume.

Trading performance is increasingly under scrutiny. The microstructure of the market is continuously changing. The fragmentation of the venues both in the lit and dark markets, whether exchanges, MTFs or systematic internalisers, as well as a diverse asset class of trades, and sub millisecond HFT algorithms in the market, all call for a new TCA framework which can be adapted to multiple asset classes within different market environments, readjust quickly and constantly in real time with inherent flexibility, and provide a comprehensive solution to optimal execution.

Conclusion

Volume based TCA fits this task and can become a pervasive solution in future. As an industry practitioner with a long innovative history, we have built the SNAP system to incorporate its benefits. From our experience, the dynamic weighting between historical values and current market information, and the fine tuning of the execution cost curve to fit trading assets and styles are crucial in the model’s successful implementation. We view it as the next generation of TCA supported by solid mathematical modelling and deep insights into market microstructure.

©Best Execution 2013