Detection and analysis of occurrences of spoofing in the Brazilian capital market

Abstract

Purpose The purpose of this paper is to analyze a data set from a brokerage firm to find possible spoofing cases in ten stocks from the Ibovespa index. The studies proposed concerned the parameters used in the search for the practice, the frequency of occurrences during the negotiation period, the impact on the price caused by the size of the spoofing order and the correlation between the stock's liquidity and the number of occurrences. Design/methodology/approach By using intraday orders flows, the authors are able to reassemble the order book and perform an analysis of potential market manipulation. Findings The authors found six possible cases, all of them happened in the beginning or end of the negotiation period, confirming that there is a window of opportunity for the practice when there is greater uncertainty related to the stock's price. Moreover, they found that in the less liquid stocks, it was necessary to place greater spoofing orders aiming to narrow the wider spread. Practical implications A methodology for spoofing detection that can be replicated by brokerage firms and other researchers was developed. Social implications The study contributes to the literature of capital market regulation by suggesting best practices for regulators and self-regulatory entities to avoid a predatory market practice. Originality/value The authors present an algorithm and parameters for detecting spoofing; other papers are not practical orientated.

Unravelling the JPMorgan spoofing case using particle physics visualization methods

“…To the best of our knowledge, there are only a handful of researchers who studied spoofing using LOB data. Mendonça and de Genaro (2020) generated 1‐min LOB snapshots from order data and used both datasets to detect spoofing on the Brazilian Stock Exchange. Leangarun et al (2016) tried to detect, among others, spoofing in three NASDAQ stock markets by training neural networks and using 1‐min LOB intervals.…”

“…For example, market participants do not know what type of order is submitted, the actual volume of an iceberg order and whether a reduction in volume is due to a cancellation or an order execution (Dalko & Wang, 2018). Spoofers can take advantage of this market microstructure by introducing conditions that can influence the decisions of other traders (Mendonça & de Genaro, 2020).…”

“…This makes nonspoofing market participants vulnerable as they are misguided by false buy and/or sell liquidity figures (Dalko & Wang, 2018). This negatively impacts the price formation process and hence distorts the price (Dalko & Wang, 2020; Mendonça & de Genaro, 2020). It also creates additional volatility in price, trading volume and order volume, which negatively impacts the stability of the market (Dalko & Wang, 2020).…”

“…It also creates additional volatility in price, trading volume and order volume, which negatively impacts the stability of the market (Dalko & Wang, 2020). Moreover, its effects can spill over into interconnected markets, making them inefficient too (Mendonça & de Genaro, 2020).…”

“…This data differs from LOB data, in that order data comprises the submitted, cancelled and modified orders of individual traders, whereas LOB data constitutes all these orders and shows the LOB visible to all market participants. For example, LOB data reveals the best bid and ask prices and total volumes belonging to specific price levels in the LOB (Mendonça & de Genaro, 2020). Although order data contains more information on individual orders (provided that it is not aggregated), studies attempting to detect spoofing with order data have omitted to reconstruct the LOB, let alone visualize it.…”