Enterprise Applications and Services in the Finance Industry - 6th International Workshop, FinanceCom 2012, Barcelona, Spain, June 10, 2012. Revised Papers
Enterprise Applications and Services in the Finance Industry - 6th International Workshop, FinanceCom 2012, Barcelona, Spain, June 10, 2012. Revised Papers
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金融行业中的企业应用程序和服务 - 第 6 届国际研讨会，FinanceCom 2012，西班牙巴塞罗那，2012 年 6 月 10 日。修订论文
DOI：
10.1007/978-3-642-36219-4_4
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Haferkorn M
中科院分区：
文献类型：
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作者：
Haferkorn M
Newcomb-Benford定律（NBL）是第一位有效数字（FSD）分布的一个众所周知的规律，因此在这一领域的研究是多方面的。截至2012年，金融市场领域的研究相当匮乏，特别是在算法交易领域。我们提出的问题是，算法交易者和人类交易者的订单提交量是否遵循NBL。这方面的结果可能有助于监管机构发现可疑的市场活动，并有助于市场参与者量化算法交易的数量。我们的研究结果表明，提交的订单量的两组遵循NBL比均匀分布。比较这两组，我们证明算法交易者比人类交易者更适合NBL，因为人类交易者倾向于过度使用FSD五。
Newcomb-Benford’s Law (NBL) is a well known regularity in the distribution of first significant digits (FSD) and therefore research in this field is manifold. As of 2012 research in the domain of financial markets is quite scarce, especially in the field of algorithmic trading. We pose the question whether order submission volumes of algorithmic traders and human traders follow NBL. Results in this context might help regulators to detect suspicious market activity and market participants to quantify the amount of algorithmic trading. Our findings indicate that the submitted order volumes of both groups follow NBL more than the uniform distribution. Comparing these two groups, we give a proof that algorithmic traders match NBL better than human traders, as human traders tend to overuse the FSD five.