文章基本信息

标题：TRADE PATTERN ON WARSAW STOCK EXCHANGE AND PREDICTION OF NUMBER OF TRADES
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作者：Henryk Gurgul ; Artur Machno
期刊名称：Statistics in Transition
印刷版ISSN：1234-7655
电子版ISSN：2450-0291
出版年度：2017
卷号：18
期号：1
页码：91-114
DOI：10.21307/stattrans-2016-059
出版社：Exeley Inc.
摘要：The main goal of this paper is to present the method for describing and predicting trade intensity on the Warsaw Stock Exchange. The approach is based on generalized linear models, the variable selection is performed using shrinkage methods such as the Lasso or Ridge regression. The variable under investigation is the number of trades of a particular stock 5-minute interval.The main conclusion is that the number of trades during short intervals is predictable in the sense that the prediction, even based on relatively simple models, is with respect to statistical properties better than the prediction based on the random walk, which is used as a benchmark model.
关键词：high frequency data;daily trade pattern;Warsaw Stock Exchange;market microstructure.