Flash Crash Research, Part 2

A couple of papers I spotted a while ago:
Easey et al study a measure of order flow toxicity called Volume-Synchronized Probability of Informed Trading in The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading. Johnson et al considers a large number of mini-flash crashes from 2006 to 2011 in Financial black swans driven by ultrafast machine ecology [PDF].

High Frequency

Wissner-Gross and Freer has a 2010 paper in Physical Review on Relativistic Statistical Arbitrage [official version, pdf]. It makes the observation that as the propagation of prices and trades reach relativistic velocities, that very relativistic limitation of propagation induces some intermediate locations (midpoints between each pair of the 52 world exchanges weighted by turnover velocity) between exchanges from which profitable arbitrage can slow or even stop information propagation. The subject of the study is a Vasicek model of cointegrating instruments based an Ornstein-Uhlenbeck process and an Ehrenfest urn model of diffusion. One hypothesized strategy has to do with offsetting positions in geographically separated exchanges. Because proving one has the correct offsetting position in a distant exchange takes too much time, a low-latency system placed in the intermediate point between two exchanges can certify offsetting positions for both exchanges.

This does remind me of another article on the proposed London-Tokyo fiber link to run under the Arctic circle. The fiber infrastructure isn't always laid according to great circle distance. Many pairs of exchanges probably are not directly connected with each other at all. Moreover, turnover varies, perhaps this means one would need a mobile or at least a chain of load-balanced low-latency systems spanning the distance between each pair of exchanges to adapt to changing conditions. Perhaps there will have to be some dynamic hedging strategies too. Regardless, the paper is an interesting look into unintended consequences of the race to zero.

Trade Frequencies, Part 2

One of the main challenges in high-frequency finance is the irregular arrival of prices. Once you get down to the tick by tick prices, you can't count on normal interval time series. There are autoregressive models which support irregular arrival of prices. Engle and Russell's autoregressive conditional duration (ACD) model comes to mind (described in Econometric Modeling of Multivariate Irregularly-Spaced High-Frequency Data which extends the Engle-Russell Autoregressive Conditional Duration (ACD) model for the multivariate case).

Finance Research Papers

Here are a few hot-off-the-press research abstracts which caught my eye:

• Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency World studies news sentiment-based high-frequency trading using an extension of Hasbrouck's market microstructure model (and Chaboud and Tookes) and NASDAQ high-frequency data from 2008 to 2009. The paper seeks to answer whether we can effectively do machine-driven trading based on news sentiment and whether speed matters. The author distinguishes between "hard" and "soft" information events where hard events are price data and soft events news ticker items, blog posts, and Twitter messages (though this paper focuses on news ticker alone). This study only uses Reuters News Sentiment software. The conclusion is that at this point, non-high frequency traders have an advantage in processing the "soft" information events. I think this area is gaining quite a bit of attention with both bigger firms (Thomas Reuters, Dow Jones) and smaller ones (a startup named Stockr.com (invite link) for example) are investing in news sentiment and social media-based trading (respectively).
• Market Making Under the Proposed Volcker Rule
• No Strings Attached: When Giving it Away Versus Making Them Pay Leads to Negative Net Benefit Perceptions in Online Exchanges

Microstructure Modeling

Jonathan Kinlay has an interesting list of papers on market microstructure including his synopsis of each. Many of them applied a vector autoregression model to quote and trade data. The focus, however, is always the impact of the limit order book and the strategies for generating new bids and asks on the limit order book. All of the studies focused on the stock markets. Perhaps the most interesting of the papers is a recent one on Price Dynamics in a Markovian Limit Order Market from Rama Cont of Columbia, which provides a mostly analytical model for high frequency dynamics of prices and order flow with endogenous relationship between durations and price changes. Most of the data in this study came from 2008. According to that data, for certain DJ stocks, ~1.2% of observed bid-ask spreads were more than 1 tick, thus not as pertinent to the model. The average lifetime of such a spread appears to be only a couple of milliseconds. The model does consider order flow in the presence of market orders and cancellations, including the case when they dominate limit orders.

I also found a number of books on the subject (more after the jump).

Trade Frequencies, Part 1

Typically what people refer to as high-frequency trading occurs on the stock markets. However, firms have also branched out to a whole assortment of other markets in attempt to reap the alpha from high-frequency strategies. Moreover, data such as futures prices sometimes serve as a signal for equity trading. Different kinds of markets, however, have considerably different characteristics. Moreover, the volume and trade frequency varies among each individual security. On one side, you have the SPY ETF which is easily among the most actively traded securities in the stock market with daily volumes in the hundreds of millions. During August 2011, SPY's volume even spiked to as much as 717 million, though 100-200 million is more typical. To put that into perspective, the total daily volume of the NYSE is about a couple billion in recent times. In contrast, some securities are barely traded at all.

More on all this after the jump...