2008 May 28 - Wed

Put Me To Sleep Reading Material

Someone in some data provider's forum was making mention of doing order flow analysis in Excel through Interactive Brokers, and the person felt that they weren't getting enough data. Which is true, Interactive Brokers sends data based upon what is necessary for someone viewing a screen, not based upon some automated data hungry automaton looking to crunch full data feeds.

That got me to thinking and to reading more about order flow analysis. This gets in to market orders, limit orders, bid/ask spreads, order books, market makers, rational traders, uninformed traders, instantaneous impact of variable sized market orders, as well as whole raft of other micro-economic activity that comes with high frequency trading.

Marco Avellaneda and Sasha Stoikov and recently released a paper entitled High-frequency trading in a limit order book, with another version of the same thing here. They develop some interesting equations on determining a bid/ask spread in the midst of a moving market, based upon a market maker's inventory and risk capability. I'm wondering if that is what BATS does for their trading capability.

Karl Ludwig Keiber has a paper called Price Discovery in the Presence of Boundedly Rational Agents. In the paper, he discusses some market maker concepts and what they deal with. Momentum as well as mean reversion are discussed in the context of bid/ask spread and price discovery. There is a minor discussion regarding adverse selection during a transition from momentum to reversal trading on page 25 which may be of some value. The cross over between reversal and momentum is a weakness in my trading.

Bruce Mizrach has a paper called The next tick on Nasdaq. Although a recently published paper, he uses data from 2002. The paper goes into some history of market making, limit books, and how Nasdaq grew up. Some of his interesting observations:

• This paper asks a surprisingly simple but neglected question: does the entire order book help predict the next inside quote revision?
• Lillo and Farmer (2004) find that orders on the London Stock Exchange follow a long memory process.
• Bouchaud et al. (2002), while analysing the Paris Bourse, found a power law for the placement of new limit orders and a hump shape for the depth in the order book.
• Weber and Rosenow (2005) find a log linear relationship between signed market order flows and returns on Island.
• I find, for example, that the number of bids or offers is more important than the quoted depth.
• In general, I find that the bids (offers) away from the inside increase the probability of a down (up) tick.
• The last result I obtain is that this volatility decreases with larger market capitalization and the presence of more market makers.
• Traders call the market makers or ECNs that frequently appear on the inside market the .ax., and they claim that taking note of the ax's activity is informativey.
• for example, the advice from the Daytrading University at http://www.daytrading-university.com/ samplesson4ways.htm. ..Even with the ECN routing that mm.s [market makers] use to hide their order flow, there.s still plenty of profitable trading to be had by correctly: (1) Avoiding buying when a major mm/ax is selling (e.g. if you see MSCO and MLCO both sitting on the inside ask you probably shouldn.t buy if their bid is three levels outside the market) and (2) .Shadowing. the ax.s buying/selling behavior, if you see that all else looks okay, e.g. no suspiciously strong ECN buying/selling on INCA/ISLD...
• The presence of a particular participant does not by itself indicate that they are significant contributors to subsequent quote revisions though.
• Looking more closely at individual participants, there are some interesting results. When ARCA takes the inside bid, the next tick is more likely to be a downtick than an uptick in 65 of 71 cases.
• When ARCA takes the inside ask, there is an uptick in 63 of 73 instances
• The effect of specific participants in the small cap market differs from the large caps. ARCA has a negative impact from the bid in all 41 cases in which it is statistically significant.
• A vector autogression can be inverted into its moving average representation, and one can then compute impulse responses functions. In our model of trades and quotes, these have the interpretation of market impact functions, or the effect on stock returns of an unexpected buy order arriving into the market.
• It can also be explained in an order driven market by what Biais et al. (1995) call the .diagonal effect. in which they observe that a limit order that improves the inside bid (ask) is more likely to be followed by another limit order which increases (decreases) the inside bid (ask). A similar diagonal effect for trades is present as well. The negative serial correlation in the small caps suggest that the quote revision process for that group can be explained without assuming informed traders,
• As in many auction designs, additional buy (sell) side interest makes the next price change more likely to be an uptick (downtick). Biais et al. (1999) observe this behaviour even in an environment in which quotes are only indicative. Similarly, in the period in which quotes are firm, the authors find that additional depth on one side of the book helps predict the appearance of additional liquidity on the same side of the book.
• The number of buyers and sellers, I find, is almost always more important than quoted depth.
• Aggregate depth, either at the inside market, or as a weighted average of the demand curve, is also helpful, and this information is surprisingly persistent. In general, the results are more successful for large cap stocks than small caps.
• Quotes away from the inside are generally not informative. Large numbers of buyers (sellers) at tiers away from the best bid (offer) are more likely to result in a downtick (uptick).
• The model of trades and quotes presented also produces dynamic estimates of market impact. The impact of a buy order can be determined beyond its impact on the current spread. The estimates appear to vary sensibly with standard measures of liquidity.

I wonder if the above snippets could be coded as in an expert system.

In Relation between Bid-Ask Spread, Impact and Volatility in Order-Driven Markets by Wyart/Bouchaud/Kockelkoren/Potters/Vettorazzo, the BATS philosophy of infinitesimal market-making can be expressed in terms of spread and the instantaneous impact of market orders. They indicate that there is an empirical correlation between the spread and the volatility per trade. As mentioned in one of the other papers, they confirm that the main determinant of the bid-ask spread is adverse selection. They also confirm that volatility comes from trade impact. The paper has an extensive bibliography worth looking into. There is an interesting corrolary in the conclusion, namely that "when the volatility per trade is large, the risk of placing limit orders is large and therefore the spread widens until limit orders become favorable."