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Flow Trading: A New Way to See the Signals Hidden in Sensitive Data
Flow trading data abounds in financial markets, as the constant churn of investment capital creates a near-limitless volume of data on a daily basis. Flow trading data, in the most general sense, is an indication of where money is moving from or to in the market. Buried within this data are signals, or market color, that analysts can mine to both retroactively explain and proactively predict market outcomes. For example, flow trading data can be used to analyze if money is moving into or out of a sector such as oil and gas, a region, or even a single stock. It’s clear why this sensitive data has considerable value to financial institutions, enabling traders to place profitable trades and drive their firm’s revenues.
From a theoretical perspective, all market participants generate and collect flow trading data. A single entity’s transactions comprise just a small portion of the market. However, the diversity of market participants means that just as the flow of capital is not evenly distributed, neither is the sensitive information associated with it. Instead, this sensitive data is concentrated around a smaller subset of highly connected nodes. These nodes are typically large sell-side institutions. Similarly, market infrastructure firms, such as exchanges, settlement venues, central counterparty clearing houses and others, all capture information about this capital and asset flow and thus have valuable sensitive data sets.
The owners of flow trading data, in all its forms, have a material theoretical advantage to understand and predict market outcomes over their less savvy peers. Think of a bulge bracket bank: Only this bank has its particular combination of consumer banking, consumer credit card, and institutional flow trading data. Most importantly, the relative inaccessibility of this sensitive trading data makes it likely that signals buried within the flow represent an edge over the near-efficient market. For example, this sensitive information can be used to create alpha.
Why Is Flow Trading Data Sensitive?
Consider a typical transaction, perhaps the sale of a stock. Data associated with this transaction would include:
- The date and time of the trade
- The security being traded
- The volume (or number) of shares traded
- The price paid per share
- The spread
- The parties involved in the trade
At first glance, none of this information may seem particularly revelatory. But now consider large volumes of this data collected across all market participants. With a little work, you may be able to glean insights on the trading strategies of a specific asset manager, or that a company is starting to struggle (or is ready to pop!) Would you want your broker to study your intended trades, uncover your strategy and then execute ahead of you driving up your price?
Protecting flow trading data is more than just protecting a name of a client: It’s about protecting all elements of a sensitive data set in a manner such that they can’t be leveraged against any position in the market. Each market participant generally considers their trades and associated attributes, including timestamps, spreads and counterparties as extremely sensitive. In the case of hedge funds, their flow trading strategies are in fact core intellectual property!
Because of this sensitivity, the holders of flow trading data must respect and ensure the confidentiality of this sensitive data and the clients involved. But this creates a fundamental conflict: How can one use this data to its fullest extent to create market color for competitive advantage, while simultaneously respecting the need for data privacy?
Securely Working with Flow Data
To derive maximum value from this data, all parties that hold it must satisfy two conflicting, but not mutually exclusive requirements. They must:
- Provide ironclad assurance that the participants in the sensitive flow trading data, and their respective entries in data sets, will remain confidential;
- Ensure that any analysis of this sensitive information will be accurate and done in near real-time.
These conflicting requirements, and the methodologies required to satisfy both of them, have historically created inefficiencies in the use of flow trading data. The table below summarizes different methodologies that can be used to protect and use flow trading data across three key dimensions: maintaining privacy, providing accurate results, and a delivering more value. By this, we mean how much data must be stripped away or “lost” to maintain client confidentiality.
Legacy data privacy-preserving techniques have often sought to achieve the privacy accuracy balance by either aggregating flow trading data or redacting it based on a manually defined ruleset. Unfortunately, this trade-off is inefficient, delivering suboptimal outcomes for either accuracy or privacy. Even worse, when these techniques are applied, client confidentiality is not truly satisfied, putting both financial institutions and their customers at risk. Another technique used to protect participants is to introduce latency, providing flow trading data as late as t+3 from the event, that is three days after a trading event occurs. While delaying the provision of sensitive trading information is an effective means to reduce the impact of a potential privacy breach, the likelihood of any signal being ‘stale’ grows as latency increases. Since flow trading advantages are fleeting, and trades are conducted in milliseconds, it makes sense that three-day-old data has little value.
Organizations that can solve this problem for their customers enable them to unlock significant business value from sensitive data sets. Financial institutions can sell or share up-to-date data, while still protecting their clients’ confidentiality.
LeapYear has pioneered a different approach to preserve the privacy of flow trading data. Rather than suppressing information or delaying or denying access to sensitive information to ensure its privacy, LeapYear uses a patented approach to ensure that any analysis of data can be automatically and rigorously kept private. By proving to both data owners and participants that each and every analysis ensures privacy (while releasing maximum information signal) we satisfy the conflicting requirements laid out above.
Create More Value from Flow Trading Data
Flow trading data is a unique and valuable data source to many in the financial services industry. This sensitive information has massive potential to enable users to generate insights and drive positive commercial outcomes. But the need to ensure the privacy and confidentiality of the participants in this data is obviously paramount.
Companies that can enable new use cases for sensitive data, while ensuring its privacy, will be able to create a competitive advantage in the market.
You can use LeapYear’s flow trading data solutions to drive more revenue for your firm and customers. To learn more about our sensitive data privacy-enhancing solutions, please contact us.
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