Limit Up–Limit Down: an effective response to the “Flash Crash”?

Abstract

Purpose The purpose of this paper is to assess the US Securities and Exchange Commission’s new regulation, Limit Up–Limit Down (LULD), against the background of manipulative high-frequency trading (HFT). Design/methodology/approach This paper examines the background of HFT and related manipulative tactics by reviewing 43 articles of empirical research. It also examines areas in which LULD is effective and those in which LULD fails. The assessment of LULD is completed with a comparison between computerized regulation and legal enforcement in the contemporary reality of electronic trading platforms. Findings The paper points out the effectiveness of LULD in regulating wild price volatility as well as its insufficiency when facing orderly but fast price momentum ignited by manipulative HFT such as “spoofing”. Practical implications The findings may provide assistance to lawmakers and regulators to improve LULD regulation. Originality/value This paper is the first attempt to assess LULD regulation against a comprehensive background of manipulative HFT. The paper is of value to other researchers concerned about the instability to the equity market that manipulative HFT can create. The paper is also of interest to policymakers in designing effective regulation in the high-frequency era.

Spoofing: effective market power building through perception alignment

“…Dalko (2016a, 2016b) proposes the PAH as a candidate mechanism to explain the causality in herding. The PAH is based on a review of the abundant literature on information-based market misconduct or manipulation.…”

“…The PAH proposed by Dalko (2016a, 2016b) aims to explain the causality for information-based herding in the financial market. It is quoted below:…”

“…This line of research implies that perception regarding future profitability plays an important role in the valuation of financial instruments and investors’ trading decisions. Therefore, individual investors are frequently guided by their perceptions when they make investment decisions (Dalko, 2016a, 2016b).…”

“…Klein et al (2012) report dozens of regulatory enforcement cases against fake orders that have triggered herding in multiple stock exchanges internationally. Dalko (2016a, 2016b) lists empirical evidence of information-based herding in several categories, such as information-based market manipulation [1], a Wall Street Journal columnist’s buy-tip-sell case, security analysts’ positive recommendation revisions and internet chats on stocks. The author suggests that perceptions about a stock’s value become a guide for numerous investors in their trading decision-making.…”

Measuring the effectiveness of volatility auctions

“…However, there is no consensus on the need for or the effectiveness of trading halts. Moreover, interest in trading halts and price limits have rekindled in the aftermath of the Flash Crash in US futures and stock markets in May, 6, 2010 (Gomber, Lutat, Haferkorn, and Zimmermann 2011;Subrahmanyam, 2013;Dalko, 2016) .…”