Trade surveillance
Cross-product manipulation in financial markets
Mike Channing
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Head of Sales, UK and EMEA
July 21, 2025
When it comes to regulatory enforcement, one of the most complex market manipulation typologies is cross-product manipulation (as well as cross-market manipulation) This involves manipulating one asset class to impact the price or perception of another related asset class. While it’s different from single-instrument manipulation, it’s also important to differentiate it from arbitrage, which is perfectly legal.
A simple example of cross-product manipulation would be traders trading in the derivatives market to influence the underlying asset’s value. The increasing interconnectivity of global financial markets and the wide variety of tradable products have opened up many opportunities for rogue traders. Historic regulations made little mention of cross-product manipulation, so they need to be updated. Then there is the issue of cross-market/cross-country cooperation.
In this article, we will look at what cross-product manipulation is, as well as some notable real-world examples. We’ll also look at the regulatory landscape surrounding it, and how trade surveillance technology plays a vital role in detecting it.
Cross-product manipulation involves using one financial product or asset to manipulate the price of another. While the instruments traded may differ in type (e.g., equity and options), they may share a common underlying reference point, and changes in the price of one can materially impact the value or settlement conditions of the other. This relationship can be exploited, for example, by trading in the cash equity market with the intention of influencing the value of a derivative or ETF linked to the same underlying security.
Cross-market manipulation occurs when a trader places orders on one trading venue or exchange to influence the price of the same or a related instrument traded on another venue or exchange. Given that a single security can be listed on multiple exchanges, this type of manipulation takes advantage of fragmented markets by exploiting the fact that orders on one venue can affect price formation on another. It is important to note that many instances of cross-market manipulation also involve cross-product dynamics, meaning the strategy may simultaneously span different product types and different trading venues.
Understanding cross-product and cross-market manipulation is essential because these strategies exploit structural characteristics of modern financial markets, particularly the fragmentation of trading venues and the proliferation of derivative products linked to common underlyings. These are not theoretical risks. Regulators such as the FCA, SEC, and ESMA have repeatedly flagged them as areas of growing concern, and enforcement actions have shown that the potential financial and reputational consequences can be severe.
One of the most notable recent examples is the FCA’s fine of more than £27 million imposed on Citigroup Global Markets Ltd in April 2025. The investigation revealed that traders at Citi engaged in cross-product manipulation by using futures contracts to influence the price of government bonds on which they also held positions.
The FCA’s investigation found that between 2017 and 2020, traders deliberately used aggressive futures orders, including spoofing, to artificially move prices in the related cash bond market, where they stood to benefit from the resulting price changes. The manipulation involved gilt futures and cash gilts, the prices of which are closely linked because gilts are the deliverable instruments underlying the futures contracts.
This enforcement action is significant not only for its size but also for its implications. It demonstrated the FCA’s willingness to pursue cross-product manipulation involving sovereign debt, a highly liquid and systemically important market segment. It also underscored the expectation that firms’ surveillance systems must be capable of monitoring interactions between related asset classes, not just isolated product lines.
The LIBOR manipulation scandal remains one of the most high-profile examples of cross-product manipulation in financial history. Major banks, including Barclays, UBS, and Deutsche Bank, were found to have colluded to manipulate the London Interbank Offered Rate, a benchmark interest rate used to price an estimated $350 trillion in financial products globally.
Traders at these institutions submitted false borrowing rate estimates with the intention of influencing the LIBOR fix. By artificially moving LIBOR, they could benefit from positions in a range of derivative products, including interest rate swaps, futures, and other contracts tied to the benchmark. The manipulation therefore operated across both products and asset classes, making it a textbook case of cross-product manipulation on a systemic scale.
The scandal led to billions of dollars in fines, criminal prosecutions, and sweeping reforms to benchmark governance, including the eventual transition from LIBOR to risk-free rates such as SOFR.
In 2020, the U.S. Department of Justice secured guilty pleas from two former JPMorgan precious metals traders for engaging in a multi-year spoofing scheme in the gold and silver futures markets. The traders placed large orders they never intended to execute in order to create the illusion of supply or demand and move prices in their favour. These spoofed orders in futures were designed to influence the perceived value of the underlying precious metals, enabling the traders to profit on their actual positions.
JPMorgan ultimately agreed to pay more than $920 million in penalties as part of a deferred prosecution agreement, making it one of the largest enforcement outcomes related to spoofing and cross-product manipulation in the U.S.
A trader builds a large out-of-the-money options position and then places manipulative orders in the underlying equity to move the share price towards the option strike, thereby inflating the option’s value prior to expiry or sale. This typology leverages the relationship between equity prices and options pricing models, such as the Black-Scholes model. Example: A trader purchases call options on a stock and then uses spoofing or layering techniques in the equity market to push the stock price up, increasing the value of the calls.
ETFs track the performance of an underlying basket of securities, and their prices are determined by the net asset value (NAV) of those components. A trader can manipulate one or more constituents of the basket to distort the ETF’s perceived value, profiting from price dislocations between the ETF and its components or from positions in the ETF itself. Example: A trader places manipulative orders in a heavily weighted constituent of an ETF near the close to move the ETF’s reference price in a favourable direction.
Index-based products derive their value from the aggregated prices of underlying instruments. By manipulating the constituent securities of an index, a trader can influence index-tracking derivatives such as futures contracts and options. Example: A trader accumulates short positions in an index future and then places large sell orders in several of the index’s most heavily weighted stocks, driving down the index value to profit from the short futures position.
A single security or instrument listed on multiple trading venues can be targeted by placing manipulative orders on one venue to influence pricing on another. This takes advantage of fragmented liquidity and the time lag in price consolidation across venues. Example: A trader places large non-genuine buy orders on a less liquid venue to create the appearance of demand, causing the price on a more liquid primary exchange to rise, where they hold a sell position.
In the foreign exchange market, certain currency pairs are mathematically related (e.g., EUR/USD and USD/JPY influence EUR/JPY). A trader may manipulate one pair to influence the value of a related pair in which they hold a position. Example: A trader with a large EUR/JPY position places spoofing orders in EUR/USD to move the rate, which in turn shifts the EUR/JPY cross rate in a favourable direction.
Cross-product manipulation is explicitly addressed within several major regulatory frameworks. Article 12(1)(b) of the EU Market Abuse Regulation (MAR) covers orders or transactions that fix prices at abnormal levels across related instruments. Similarly, Section 9(a)(2) of the U.S. Securities Exchange Act prohibits transactions designed to manipulate the price of securities. In the UK, the FCA’s Code of Market Conduct (MAR 1.6) provides guidance on what constitutes market manipulation, including behaviour involving related products or instruments.
Regulatory expectations have become more specific in recent years. The FCA’s Market Watch publications, including Market Watch 79, have highlighted firms’ failures to monitor cross-product interactions. ESMA’s final report on MAR has similarly flagged the need for more robust cross-product surveillance. In the U.S., FINRA and the CFTC have both brought enforcement actions involving cross-product schemes, particularly in derivatives and commodities markets.
Detecting cross-product manipulation requires surveillance systems capable of correlating trading activity across different instruments, asset classes, and venues. Traditional single-instrument monitoring is insufficient, as the manipulative intent can only be identified by analysing how activity in one product affects another. A cross-product market abuse and trade surveillance solution must correlate order book activity across related products, assess whether anomalous pricing in one instrument can be linked to trading behaviour in another, and apply scenario-based detection models tailored to the specific product relationships involved.
Advanced surveillance systems use statistical methods, correlation analysis, and increasingly, machine learning models to surface potential cross-product manipulation. These systems need to operate across fragmented data sets, integrating order flow, execution data, and market data from multiple venues and asset classes in near real-time.
Firms that fail to implement cross-product surveillance expose themselves to the risk of undetected manipulation within their own order flow, regulatory censure for inadequate systems and controls, and reputational harm, particularly if enforcement action becomes public.
Cross-product manipulation remains one of the most challenging forms of market abuse to detect and regulate. As financial markets become more interconnected and the range of tradable products continues to expand, the opportunity for exploitation of cross-product relationships will only grow. Regulatory enforcement is intensifying, as demonstrated by the Citigroup case and other recent actions, and firms are expected to keep pace with these developments by maintaining sophisticated, multi-product surveillance capabilities.
For market participants, the message is clear: single-instrument monitoring is no longer adequate. Firms must invest in surveillance systems that can operate across product types, venues, and jurisdictions to meet both their regulatory obligations and the broader expectations of financial markets. If you are concerned about your regulatory obligations going forward, please contact us, and we can discuss your options in detail.
