Is there data on market participants at a particular moment?

Question

I am looking for data on market participants at a particular moment (or some proxy/approximation). For example, how can I tell whether mostly big players and HFTs are dominating the market in particular time frame or whether there is a considerable amount of "human" trading. I would like this data at as high a frequency as possible.

Any ideas/explanations/consideration/pointers to data providers would be highly appreciated.

Answer 1

the Commodity Traders report is the most useful for this, it lets you deduce large and small players on the stock index futures. This is only released weekly by the CFTC

Otherwise you can use volume:price divergence and average volume moving average to further deduce whats happening. Finally you can use level 2's to get a feel for the speed of orders and liquidity in an equity.

Answer 2

Your question will be very difficult to answer, at least for equities. The best you can probably do in terms of accurate information are research reports from organizations like Tabb. You can look at positioning of players from 13F reports, meaning you can see which players have large positions in a certain equity. You may not be able to discern why, especially for large players who run many different type of strategies.

As far as daily information I know of no reliable way to see what type of player is trading what. Its easy to see which instruments HFT is in from the quote volume and patterns. Otherwise for equities the players are hidden behind the order books.

Answer 3

A possible answer is the approach of Hendershott, Jones, and Menkveld in their JF2011 paper (this paper was mentioned also in this post). From the introduction:

We use a normalized measure of NYSE electronic message traffic as a proxy for AT. This message traffic includes electronic order submissions, cancellations, and trade reports. Because we normalize by trading volume, variation in our AT measure is driven mainly by variation in limit order submissions and cancellations. This means that, for the most part, our measure is picking up variation in algorithmic liquidity supply.

They discuss their proxy for AT (Algorithmic Trading) in section II.A. The main intuition - as stated in the intro - is that the ratio between messages and executions has increased because of AT, through the various practices it allows (e.g.: fast repositioning of limit orders and cancellations).

[F]or each stock each month we calculate our AT proxy, algo tradit, as the number of electronic messages per $100 of trading volume.