Modeling the trading volume curves of financial instruments throughout the
day is of key interest in financial trading applications. Predictions of these
so-called volume profiles guide trade execution strategies, for example, a
common strategy is to trade a desired quantity across many orders in line with
the expected volume curve throughout the day so as not to impact the price of
the instrument. The volume curves (for each day) are naturally grouped by stock
and can be further gathered into higher-level groupings, such as by industry.
In order to model such admixtures of volume curves, we introduce a hierarchical
Poisson process model for the intensity functions of admixtures of inhomogenous
Poisson processes, which represent the trading times of the stock throughout
the day. The model is based on the hierarchical Dirichlet process, and an
efficient Markov Chain Monte Carlo (MCMC) algorithm is derived following the
slice sampling framework for Bayesian nonparametric mixture models. We
demonstrate the method on datasets of different stocks from the Trade and Quote
repository maintained by Wharton Research Data Services, including the most
liquid stock on the NASDAQ stock exchange, Apple, demonstrating the scalability
of the approach.

金融交易应用中，建模金融工具的交易量曲线是一个关键的研究领域，该论文介绍了一种基于层次化泊松过程模型的交易量曲线建模方法，利用贝叶斯非参数混合模型的切片抽样框架推导出高效的马尔可夫链蒙特卡洛算法，实证研究表明该方法在不同股票数据集上的可扩展性。