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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14289
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| Title: | Uncovering Hidden Structure in Bond Futures Trading |
| Authors: | Chen, Fei
Figlewski, Stephen
Heisler, Jeffrey
Weigend, Andreas S. |
| Issue Date: | Jan-1998 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-98-01 |
| Abstract: | This study uncovers trading styles in the transaction records of US
Treasury bond futures. It uses transaction-by-transaction data from the
Commodity Futures Trading Commissions' (CFTC) Computerized Trade
Reconstruction (CTR) records. The data set consists of 30 million
transaction - the complete US T-bond futures market for 3 years. Each
transaction record consists of time (by the minute), price, volume,
buy/sell, and an identifier of the specific account. We use statistical
clustering techniques to group together trades that are similar. Two
sets of assumptions have to be made: (1) What is a trade? We define a
trade to begin when an account opens a position, and to end when its
position size returns to zero. We describe each trade by several
trade-specific variables (e.g., length of trade, maximum position size,
opening move, long or short) and several exogenous, market-specific
variables (e.g., price, volatility, trading volume). (2) What process
generated the data? We assume a mixture of Gaussians. An observed trade
is interpreted as a noisy realization of one of the mixture components.
This paper assumes identity covariance matrices. Furthermore, each trade
is fully assigned to a single cluster. We compare this approach to
diagonal and to full covariance structure with probabilistic
assignments. Trade profit was held back in the clustering process. It
turns out that the clusters differ significantly in their profit and
risk characteristics. Using conditional distributions, we summarize
features of profitable trading styles and contrast them with losing
strategies. We find that profitable styles tend to hold trades longer,
trade at higher volatility, and trade earlier in the contracts. We also
show how some clusters uncover "technical" traders. Using the
information about the individual accounts, the assignments of accounts
to clusters are described by entropy, and the transitions of a given
account through clusters is modeled by a first order Markov model. |
| URI: | http://hdl.handle.net/2451/14289 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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