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Applying Supervised Machine Learning Models to Classify Day Traders Using the Traders' Daily Trading Activity Data and the U.S. Stock Market Indices Data

机译：应用监督机器学习模型以使用交易者的每日交易活动数据和美国股票市场指数数据对日间交易者进行分类

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作者
Sharara, Nasser Ateya;
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作者单位

The George Washington University;

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授予单位 The George Washington University;
学科
学位 D.Engr.
年度 2018
页码 136 p.
总页数 136
原文格式 PDF
正文语种 eng
中图分类
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专利

1. Identifying the interaction between stock market returns and trading flows of investor types: Looking into the day using daily data [J] . Numan UEkue, Enzo Weber Journal of banking & finance . 2013,第8期

机译：确定股票市场收益率和投资者类型的交易流之间的相互作用：使用每日数据来观察一天
2. Natural gas futures had a volatile day, first falling sharply amid bearish storage data, then nearly recovering at the end of trading as gas futures traders refocused on colder weather forecasts for the balance of the month [J] . Gas Market Reconnaissance Group NGW's Gas Market Reconnaissance . 2010,第Nova18期

机译：天然气期货市场动荡，首先在存储数据看跌的情况下急剧下跌，然后在交易结束时几乎恢复，因为天然气期货交易员将注意力集中在当月余下的较冷的天气预报上
3. Market trader heterogeneity and high frequency volatility dynamics: further evidence from intra-day FTSE-100 futures data [J] . David G. McMillan, Alan E. H. Speight Applied Financial Economics Letters . 2006,第2期

机译：市场交易者的异质性和高频波动性动态：来自日内FTSE-100期货数据的进一步证据
4. The Impact of Day Trading on Traders' Profitability and Market Quality: Results from agent-based simulations of China's stock market [C] . He_ying JIAO, Shan_cun LIU, Wei CHENG 2010 IEEE international conference on software engineering and service sciences. . 2010

机译：日间交易对交易者的获利能力和市场质量的影响：基于代理商的中国股市模拟结果
5. Applying Machine Learning with Spatio Temporal Analysis to Classify Crowdsourced Data from the 2010 Haiti Earthquake Relief Efforts. [D] . Jamal, Sarosh. 2017

机译：将机器学习与时空时空分析相结合，对2010年海地地震救济努力中的众包数据进行分类。
6. Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study [O] . Charu Sharma, Amber Habib 2012

机译：基于互信息的股票网络和使用高频数据的日内交易者的投资组合选择：印度市场案例研究
7. We develop a sequential trade model of Iceberg order execution in a limit order book. The Iceberg-trader has the freedom to expose his trading intentions or (partially) shield the true order size against other market participants. Order exposure can cause drastic market reactions (“market impact”) in the end leading to higher transaction costs. On the other hand the Iceberg trader faces a loss-in-priority when he hides his intentions, as most electronic limit order books penalize the usage of hidden liquidity. Thus the Iceberg-trader is faced with the problem to find the right trade-off. Our model provides optimal exposure strategies for Iceberg traders in limit order book markets. In particular, we provide a range of analytical statements that are in line with recent empirical findings on the determinants of trader’s exposure strategies. In this framework, we also study the market impact also market impact of limit orders. We provide optimal exposure profiles for a range of hightech stocks from the US SP500 and how they scale with the state-of-the-book. We finally test the Iceberg’s performance against the limit orders and find that Iceberg orders can significantly enhance trade performance by up to 60. [O] . Gökhan Cebiro˜glu, Ulrich Horst 100

机译：我们在限价订单簿中开发了冰山订单执行的顺序交易模型。 Iceberg-trader可以自由地揭露他的交易意图，或者（部分）屏蔽其他市场参与者的真实订单规模。订单暴露最终会导致市场反应激烈（“市场影响”），导致交易成本上升。另一方面，冰山交易员在隐藏他的意图时面临优先权损失，因为大多数电子限价订单都会惩罚隐藏流动性的使用。因此，冰山商人面临着找到正确权衡的问题。我们的模型为限额订单市场中的冰山交易者提供了最佳的风险敞口策略。特别是，我们提供了一系列分析性陈述，这些陈述与最近关于交易者暴露策略决定因素的实证研究结果一致。在此框架下，我们还研究了市场影响以及限价订单的市场影响。我们为来自美国标准普尔500指数的一系列高科技股票提供最佳曝光配置文件，以及它们如何随着本书的规模扩展。我们最终根据极限订单测试了冰山的表现，发现冰山订单可以显着提升贸易业绩高达60％。

Applying Supervised Machine Learning Models to Classify Day Traders Using the Traders' Daily Trading Activity Data and the U.S. Stock Market Indices Data

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