Reducing transaction costs using intraday forecasts of limit order book slopes

IF 3.4 3区 经济学 Q1 ECONOMICS
Chahid Ahabchane, Tolga Cenesizoglu, Gunnar Grass, Sanjay Dominik Jena
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引用次数: 0

Abstract

Market participants who need to trade a significant number of securities within a given period can face high transaction costs. In this paper, we document how improvements in intraday liquidity forecasts can help reduce total transaction costs. We compare various approaches for forecasting intraday transaction costs, including autoregressive and machine learning models, using comprehensive ultra-high-frequency limit order book data for a sample of NYSE stocks from 2002 to 2012. Our results indicate that improved liquidity forecasts can significantly decrease total transaction costs. Simple models capturing seasonality in market liquidity tend to outperform alternative models.

Abstract Image

利用限价订单簿斜率的盘中预测降低交易成本
需要在一定时期内交易大量证券的市场参与者可能会面临高昂的交易成本。在本文中,我们记录了盘中流动性预测的改进如何有助于降低总交易成本。我们使用 2002 年至 2012 年纽约证券交易所股票样本的全面超高频限价订单簿数据,比较了预测盘中交易成本的各种方法,包括自回归模型和机器学习模型。我们的研究结果表明,改进流动性预测可以显著降低总交易成本。捕捉市场流动性季节性的简单模型往往优于其他模型。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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