  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.
