Insider Trading and Market Structure

Yesha Yadav
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引用次数: 11

Abstract

This Article argues that the emergence of algorithmic trading raises a new challenge for the law and policy of insider trading. It shows that securities markets comprise a cohort of algorithmic “structural insiders” that – by virtue of speed and physical proximity to exchanges – systematically gain first access to information and play an outsize role in price formation. This Article makes three contributions. First, it introduces and develops the concept of structural insider trading. Securities markets increasingly rely on automated traders utilizing algorithms – or pre-programmed electronic instructions – for trading. Policy allows traders to enjoy important structural advantages: (i) to physically locate on or next to an exchange, shortening the time it takes for information to travel to and from the marketplace; and (ii) to receive feeds of richly detailed data directly to these co-located trading operations. With algorithms sophisticated enough to respond instantly and independently to new information, co-located automated traders can receive and trade on not-fully-public information ahead of other investors. Secondly, this Article shows that structural insider trading exhibits harms that are substantially similar to those regulated under conventional theories of corporate insider trading. Structural insiders place other investors at a persistent informational disadvantage. Through their first sight of market-moving data, structural insiders can capture the best trades and erode the profits of informed traders, reducing their incentives to participate in the marketplace. Despite the similarity in harms, however, this Article shows that doctrine does not apply to restrict structural insider trading. Rather, structural insiders thrive in full view and with regulatory permission. Thirdly, the Article explores the implications of structural insider trading for the theory and doctrine of insider trading. It shows them to be increasingly incoherent in their application. In protecting investors against one set of insiders but not another, law and policy appear under profound strain in the face of innovative markets.
内幕交易与市场结构
本文认为,算法交易的出现对内幕交易的法律和政策提出了新的挑战。它表明,证券市场由一群算法上的“结构性内幕人士”组成,凭借速度和距离交易所的物理距离,他们系统地率先获得了信息,并在价格形成中发挥了巨大作用。这篇文章有三个贡献。首先,引入并发展了结构性内幕交易的概念。证券市场越来越依赖于利用算法(或预先编程的电子指令)进行交易的自动交易员。政策允许交易者享受重要的结构性优势:(i)实际定位在交易所上或旁边,缩短了信息往返市场所需的时间;(ii)直接向这些位于同一地点的交易操作接收丰富详细的数据。由于算法足够复杂,可以对新信息做出即时和独立的反应,协同位置的自动交易员可以比其他投资者更早接收和交易不完全公开的信息。其次,本文表明,结构性内幕交易所表现出的危害与传统公司内幕交易理论所规定的危害大体相似。结构性内部人士使其他投资者长期处于信息劣势。通过第一眼看到影响市场走势的数据,结构性内幕人士可以捕捉到最好的交易,侵蚀知情交易员的利润,降低他们参与市场的动力。然而,尽管在危害上相似,本文表明该原则并不适用于限制结构性内幕交易。相反,结构性内部人士在众目睽睽之下,在监管机构的许可下茁壮成长。第三,本文探讨了结构性内幕交易对内幕交易理论和学说的启示。这表明它们在应用中越来越不连贯。面对创新市场,法律和政策在保护投资者免受一群内部人士的侵害而不是另一群内部人士的侵害方面,似乎面临着巨大的压力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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