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首页> 外文期刊>International Journal of Electronic Commerce >Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming
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Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming

机译：使用高频交易数据预测金融市场：用强类型遗传编程检查

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摘要

Market regulators around the world are still debating whether high-frequency trading (HFT) plays a positive or negative role in market quality. We develop an artificial futures market populated with high-frequency traders (HFTs) and institutional traders using Strongly Typed Genetic Programming (STGP) trading algorithm. We simulate real-life futures trading at the millisecond time frame by applying STGP to E-Mini S&P 500 data stamped at the millisecond interval. A direct forecasting comparison between HFTs and institutional traders indicate the superiority of the former. We observe that the negative implications of high-frequency trading in futures markets can be mitigated by introducing a minimum resting trading period of less than 50 milliseconds. Overall, we contribute to the e-commerce literature by showing that minimum resting trading order period of less than 50 milliseconds could lead to HFTs facing a queuing risk resulting in a less harmful market quality effect. One practical implication of our study is that we demonstrate that market regulators and/or e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct trading behavior-based profiling. This can be used to detect the occurrence of new HFT strategies and examine their impact on the futures market.
机译：世界各地的市场监管机构仍在争论是否高频交易（HFT）在市场质量方面发挥积极或负面作用。我们使用强类型的遗传编程（STGP）交易算法，开发一个拥有高频交易员（HFTS）和机构交易商的人为期货市场。我们通过将STGP应用于毫秒间隔盖紧的E-Mini S＆P 500数据来模拟现实期货交易。 HFTS与机构交易员之间的直接预测比较表明前者的优势。我们遵守期货市场高频交易在期货市场中的负面影响，可以通过引入小于50毫秒的最低休息时间来减轻。总体而言，我们通过表明最低休息交易顺序期间的休息时间低于50毫秒可能导致面临排队风险的哈夫特，从而导致持续有害的市场质量效应。我们研究的一个实际意义是，我们证明市场监管机构和/或电子商务从业者可以应用人工智能工具，如STGP，以进行基于交易行为的分析。这可用于检测新的HFT策略的发生，并检查他们对期货市场的影响。

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