Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism

TL;DR

This paper introduces a novel ultra-high-frequency duration prediction framework combining LSTM networks and attention mechanisms to improve the estimation of transaction durations in financial markets, enhancing liquidity risk assessment.

Contribution

The study develops a hybrid LSTM and attention-based model extending the ACD framework, providing probabilistic inference and better interpretability for transaction duration prediction.

Findings

The proposed model outperforms traditional methods on large-scale datasets.

Attention mechanism highlights key temporal positions influencing duration predictions.

The hybrid approach improves liquidity risk measurement accuracy.

Abstract

The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p({\Delta}t_(i+1) |G_i) where {\Delta}t_(i+1) represents the duration of the (i+1)-th transaction, G_i represents the historical information at the time when the (i+1)-th transaction occurs. In this paper, we propose a new ultra-high-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.