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

Brière, Marie; Lehalle, Charles‐Albert; Nefedova, Tamara; Raboun, Amine

doi:10.2139/ssrn.3420665

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2021

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“…They find that trade sign, market order size, and liquidity based on best limit order prices tend to be selected as the most relevant features. Brière et al (2019) use Bayesian networks to forecast implementation shortfall as a measure of transaction cost. This approach is particularly useful in cases of missing data because it can impute the most probable value given the available information.…”

Section: Executionmentioning

confidence: 99%

Machine Learning for Active Portfolio Management

Bartram

Branke

Rossi³

et al. 2021

JFDS

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KEY FINDINGSn Machine learning (ML) methods have several advantages that can lead to successful applications in active portfolio management, including the ability to capture nonlinear patterns and a focus on prediction through ensemble learning.n ML methods can be applied to different steps of the investment process, including signal generation, portfolio construction, and trade execution, with reinforcement learning expected to play a more significant role in the industry.n Empirically, the investment performance of ML-based active exchange-traded funds is mixed.

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Section: Executionmentioning

confidence: 99%