How Algorithmic Trading Undermines Efficiency in Capital Markets

Yesha Yadav
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引用次数: 24

Abstract

This Article argues that the rise of algorithmic trading profoundly challenges the foundation on which much of today’s securities regulation framework rests: the understanding that securities’ prices objectively reflect available information in the market. The Efficient Capital Markets Hypothesis (ECMH) has long provided the theoretical touchstone undergirding central pillars of securities regulation. Mandatory disclosure, evidentiary presumptions in anti-fraud litigation and regulation driving the design of modern exchanges all look to the ECMH for theoretical validation. It is easy to understand why. Laws that make markets better at interpreting information can also improve their ability to allocate capital across the real economy.Theory and regulation have failed to keep pace with markets where traders rely on pre-programmed algorithms to execute trades. This Article makes two claims. First, complex algorithms foster a separation between the trader and her ability to fully control the operation of the algorithm. Algorithms can execute many thousands of trades in milliseconds, crunching vast quantities of data and dynamically interacting with other traders in the process. This intelligence makes it difficult for a trader to fully predict how an algorithm might behave ex ante and near-impossible for her to track and control its activities in real-time. Secondly, though markets have traditionally relied on informed fundamental traders to decode complexity, these actors now possess reduced incentives to perform this function in algorithmic markets. Fundamental traders routinely see their gains diminished by faster, automated counterparts, able to front-run trades and to derive maximal benefit from the research of others. In arguing that algorithmic trading is transforming how markets process and interpret information, this Article shows that conventional assumptions in securities law doctrine and policy also break down. With these insights, this Article, offers a new framework to thoroughly reevaluate the centrality of efficiency economics in regulatory design.
算法交易如何破坏资本市场的效率
本文认为,算法交易的兴起深刻地挑战了当今大部分证券监管框架所依赖的基础:对证券价格客观反映市场中可用信息的理解。长期以来,有效资本市场假说(ECMH)一直是支撑证券监管核心支柱的理论试金石。强制披露、反欺诈诉讼中的证据假设,以及推动现代交易所设计的监管,都在寻求ECMH的理论验证。原因很容易理解。让市场更好地解读信息的法律,也能提高它们在整个实体经济中配置资本的能力。理论和监管未能跟上市场的步伐,因为交易员依赖预先编程的算法来执行交易。本文提出了两点主张。首先,复杂的算法将交易者与其完全控制算法操作的能力分离开来。算法可以在几毫秒内执行数千笔交易,处理大量数据,并在此过程中与其他交易者动态交互。这种智能使得交易者很难事先完全预测算法的行为,并且几乎不可能实时跟踪和控制算法的活动。其次,尽管市场传统上依赖消息灵通的基本面交易员来解读复杂性,但这些参与者现在在算法市场中履行这一职能的动机降低了。基本面交易者经常看到他们的收益被更快的、自动化的对手所减少,他们能够提前交易,并从其他人的研究中获得最大的利益。在论证算法交易正在改变市场处理和解释信息的方式时,本文表明,证券法理论和政策中的传统假设也被打破。有了这些见解,本文提供了一个新的框架来彻底重新评估效率经济学在监管设计中的中心地位。
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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