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...