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by Aidan O’Neill
Kx has a broad list of products and solutions built on the time-series database platform kdb+ that capitalize on its high-performance capabilities when analyzing very large datasets.
Kx for Surveillance is a robust platform widely used by financial institutions for monitoring trades for regulatory compliance. The Surveillance platform instantly detects known trading violations like layering, spoofing or marking the close. Customers can calibrate their parameters in real time to improve their detection quality and accuracy. The flexibility of the historical database and replay engine eases retrospective investigation for new types of fraudulent behavior and suspicious activity.
In this series, we take a look at what makes Kx for Surveillance such a powerful tool for detecting market manipulation. This second article in the series takes a close look at inter-trading alerts.
The market manipulation practice referred to in this blog as “inter-trading” is known by many other names too, including cross-product manipulation, cross-asset manipulation, cross-instrument manipulation and the basic concept even extends to the practice of cross-venue manipulation. The strategy is relatively simple – the manipulation of prices on one market in order to make profits on another. It typically involves influencing the price of a stock in order to profit from trades in an option on the same underlying, but in theory could be applied to any related assets, even the same asset traded on different venues, sometimes referred to as cross-venue manipulation
A 2017 prosecution by the U.S. Securities and Exchange Commission (SEC) provides an excellent case study on the practice. It involved Lek Securities Corporation, its parent company Avalon FA Ltd and their founders were alleged to have engaged part in both layering and inter-trading schemes, from 2010 through to 2016. The SEC filing succinctly outlines the inter-trading charges:
“Avalon bought and sold U.S. stock at a loss for the purpose of moving the prices of corresponding options, so that Avalon could make a profit by trading those options at artificial prices that they would not have been able to obtain but for the manipulation. Avalon’s stock trades had no legitimate economic reason, and were intended to inject into the market false information about supply and demand in order to move the prices of corresponding options to artificial levels.”
The filing further outlines Avalon’s broader strategy; accepting losses on the underlying stock and repeating the strategy over an array of assets in order to obtain a long-term profit:
“Although the strategy involved taking a loss on the stock transactions, such losses were far outweighed by Avalon’s significant profits from trading the corresponding options whose prices Avalon had manipulated. Avalon engaged in hundreds of instances of cross-market manipulation involving numerous stocks and options from at least August 2012 through at least December 2015, and Avalon made millions of dollars in profits from that scheme.”
The SEC alleges more than $28 million in illicit profits were generated, the case is pending.
The Data Challenge
The advent of algorithmic trading, and the high-frequency trading that it facilitated, resulted in a massive rise in market volumes, a more than seven-fold increase between 1998 and 2016 (to over 7 billion daily average volume in the US alone). This has made manipulative practices all the harder to discern amid the noise of regular trading activity, and is never more true than for inter-trading where multiple assets or venues must be considered in unison. This highlights the need for a technology that can process massive volumes of data quickly and efficiently, and a surveillance solution that provides an appropriate toolbox for market manipulation analysis.
Market surveillance toolbox
But number crunching power is just one side of the coin, it needs to be matched by an able toolset for surveillance analysis. Analysts need the ability to identify trends, manage cases and visualize potential instances of market manipulation for efficient analysis.
To that end intuitive visuals are incredibly important to analysts who may review hundreds of potential market violations each day. They should paint a clear picture of the infringement under investigation, highlighting the behavior of the ‘market impacting’ participant versus the wider market.
Below is an example of the Kx for Surveillance cross-asset investigation screen. It demonstrates a clear pattern of ‘market impacting’ buy -side activity influencing the price of an underlying (top chart), while the same participant benefits on their trading of an associated derivative (bottom chart). This investigation screen gives analysts a quick and clear insight into the market moving participant’s impact on both markets.
The investigation screen depicts EUR/USD trading by a market participant in both the underlying asset and a derivative market. The participant enters the derivative market, shortly before starting a series of executions on the underlying market. Their buy activity in the underlying market encouraged an upward price movement there, and was followed by a similar rise in the derivative market. When they could execute to a profit in the derivative by liquidating their position, they did – a clear indication of inter-trading.
The processing power of kdb+ is unparalleled and is consistently the top performing time-series database according to STAC Research. Its ability to perform complex, vectorized calculations is well matched to the challenges posed by market manipulation analysis. For instance, consider the requirement of calculating statistically significant price movements in an underlying stock in inter-trading analysis, across potentially tens of thousands of underlying assets. Hugh Hyndman’s excellent analysis on kdb+ performance versus competitors (https://kx.com/benchmarking/) outlines kdb+’s power in performing complex, vector operations, often outperforming competitors by thousands of queries per second.
kdb+’s handling of nanosecond granularity and join operations designed for analyzing financial transactions, like asof and window join, also lend themselves to market surveillance. In the context of inter-trading, these natively provide the most granular view possible on transactions across multiple assets – allowing speedy analysis on a firms trading activity on, say, AAPL in tandem with their trading on its options. Ultimately, this along with its statistical processing power makes kdb+ a natural choice for the challenges posed by market surveillance.
SEC filing against Lek Securities Corporation: https://www.sec.gov/litigation/complaints/2017/comp-pr2017-63.pdf
Market volume relation to algorithmic trading: https://www.marketwatch.com/story/high-frequency-trading-has-reshaped-wall-street-in-its-image-2017-03-15
Spoofing and Layering blog by Jamie O’Mahony: https://kx.com/blog/kx-product-insights-spoofing-and-layering-alerts/
For more information on Kx for Surveillance and its functionality please click on the links below.