Modelling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance

36 Pages Posted: 17 Jul 2019

See all articles by Marie Briere

Marie Briere

Amundi Asset Management; Paris Dauphine University; Université Libre de Bruxelles

Charles‐Albert Lehalle

Capital Fund Management

Tamara Nefedova

Université Paris-Dauphine

Amine Raboun

Euronext Paris; Université Paris Dauphine

Date Written: July 16, 2019

Abstract

Using a large database of US institutional investors’ trades in the equity market, this paper explores the effect of simultaneous executions on trading cost. We design a Bayesian network modelling the inter-dependencies between investors’ transaction costs, stock characteristics (bid-ask spread, turnover and volatility), meta-order attributes (side and size of the trade) and market pressure during execution, measured by the net order flow imbalance of investors meta-orders. Unlike standard machine learning algorithms, Bayesian networks are able to account for explicit inter-dependencies between variables. They also prove to be robust to missing values, as they are able to restore their most probable value given the state of the world. Order flow imbalance being only partially observable (on a subset of trades or with a delay), we show how to design a Bayesian network to infer its distribution and how to use this information to estimate transaction costs. Our model provides better predictions than standard (OLS) models. The forecasting error is smaller and decreases with the investors' order size, as large orders are more informative on the aggregate order flow imbalance R2 increases out-of-sample from -0.17% to 2.39% for the smallest to the largest decile of order size). Finally, we show that the accuracy of transaction costs forecasts depends heavily on stock volatility, with a coefficient of 0.78.

Keywords: Trading Costs, Liquidity, Crowding, Bayesian Networks

JEL Classification: G12, G14

Suggested Citation

Briere, Marie and Lehalle, Charles‐Albert and Nefedova, Tamara and Raboun, Amine, Modelling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance (July 16, 2019). Available at SSRN: https://ssrn.com/abstract=3420665 or http://dx.doi.org/10.2139/ssrn.3420665

Marie Briere

Amundi Asset Management ( email )

90 Boulevard Pasteur
Paris, 75015
France

Paris Dauphine University ( email )

Université Libre de Bruxelles ( email )

Brussels
Belgium

Charles‐Albert Lehalle

Capital Fund Management

23 rue de l'Université
Paris, 75007
France

Tamara Nefedova

Université Paris-Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

HOME PAGE: http://www.tamaranefedova.com

Amine Raboun (Contact Author)

Euronext Paris ( email )

14 Place des Reflets
Courbevoie, 92400
France
0170482400 (Phone)

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

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