Abstract
In stock trading markets, trade duration (i. e., inter-arrival times of trades) usually exhibits high uncertainty and excessive zero values. To forecast conditional distribution of trade duration, this study proposes a hybrid model called “DL-ZIACD” for short, which addresses the problem of excessive zero values by a zero-inflated distribution. Meanwhile, dynamics of the distribution time-varying parameters are captured by a specially designed deep learning (DL) architecture in which the behavioral patterns of large traders and small individual traders are represented separately by different blocks. The proposed hybrid model takes advantage of the strong fitting ability of deep learning methods while allowing for providing a probabilistic output. This paper empirically applied the established model to a large-scale dataset, containing 9,900,000 transactions of the Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) constituents. To the best of our knowledge, no previous studies have applied conditional duration models to a dataset of such a large scale. For both the central location forecasting and the extreme quantile forecasting, our proposed model exhibited significant superiority over the benchmark models, which indicates that our DL-ZIACD model can provide accurate forecasts in conditional duration distribution.
Highlights
In the electronic security trading system, limit orders are offered by potential buyers and sellers
The established hybrid deep learning (DL)-ZIACD model is applied to most constituent stocks of the Chinese Shenzhen Stock Exchange 100 Index (SZSE 100), and the results show that our DL-ZIACD model is superior to the benchmark models in forecasting conditional duration distribution
By training the parameters of the DL-ZIACD model based on Algorithm 1, we can forecast the conditional duration distribution function ∧gi for each transaction in the future and acquire the quantiles of ∧gi
Summary
In the electronic security trading system, limit orders are offered by potential buyers and sellers. A trade will be executed only if the maximum bid price from the buy limit orders is higher than the minimum asked price from the sell limit orders. This results in a high uncertainty of trade duration. In order to model the duration sequences, researchers most use the autoregressive conditional duration (ACD) model [2], in which the duration is assumed to be the multiplication of conditional mean duration and an error term. Various studies were conducted to extend the classic ACD model from two perspectives. The researchers in Refs. [3,4,5,6] focused on extending
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