The Market’s Most Valuable Asset?

DTCC’s Tim Lind writes that using data effectively can provide the intelligence needed to improve investment performance, reduce risk and inform strategic decision‑making. 

This article was originally published in WatersTechnology on August 27, 2026. 

Capital markets is an industry that primarily transacts in information and tirelessly searches for risk-weighted returns across millions of different options and scenarios. Understanding of risk and opportunity is increasingly reliant on empirical and historical data to drive quantitative methods rather than relying on fundamental analysis and human judgment of how macroeconomic forces drive investment decisions.  

Institutional asset management is shifting toward a mosaic of quantitative, fundamental, and systematic trading that will alter the type of tools and talent needed to remain competitive. Firms must move beyond the familiar “data is the new oil” cliché. However, it’s clear that if information is the industry’s product, then data is certainly its currency.  

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Institutional investors including pension schemes, sovereign wealth funds, and fund management firms are following strategies originally curated in the hedge fund community by allocating larger percentages of their assets under management toward quantitative and systematic strategies.  

Algorithmic models that depend on mathematical equations and empirical inputs are becoming the predominant framework to manage money. These frameworks include the use of statistical, factor-based strategies that consider many more inputs than a manual operator could possibly entertain.  

Quants are looking to isolate the underpinning dynamics of equity pricing supply and demand, which are driving momentum, volatility, concentration, and how various actors are participating in a market. Ultimately, historical trading data helps explain the specific characteristics of how a stock trades and patterns have contributed to its overall performance.  

As sophistication evolves, models trend towards finding additional inputs and variables that will lead to stronger relative performance. Looking at the market structure of an asset class and the dynamics of who participates in that market are key to understanding momentum and demand. Passive investing, high-frequency trading, derivatives, and exchange-traded funds are all having a massive impact on pricing dynamics. The ability to consider each source of supply and momentum as well as what is driving volumes is critical to understanding true liquidity and conviction.  

Data as the Differentiator  

ETFs, the most impactful product innovation of our generation, have evolved from an efficient way of finding exposure to constituents of the market to actually being the primary driver of pricing dynamics. As the creation and redemption of underlying constituents try to keep pace with liquid ETF wrappers, trillions of dollars slosh back and forth in an algorithmic arbitrage machine that has evolved into one of the most important dynamics of market structure. You can’t understand markets without isolating the impact of ETFs. ETFs are also impacting fixed-income markets with similar liquidity and pricing dynamics that will drive increased electronic trading.  

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The impact of AI is too broad to detail here, but AI is accelerating experimentation, allowing firms to test model changes in days or hours instead of weeks or months. Cloud platforms, advanced analytics, and data visualization tools have made it easier to scale computation of large datasets and extract insights more efficiently. AI and cloud are propelling innovation but remain dependent on historical and empirical data. Asset allocation and performance will rely on data availability as much as the genius of the model itself.  

In practice, what separates useful market data from noise is its ability to capture where, what size and what price liquidity is forming across instruments. The volume of available information has grown exponentially. Trading activity, settlement flows, lifecycle events and reference data are generated continuously across asset classes, primarily in silos. The market’s most valuable data must be trusted with timely transactional activity grounded in accuracy.  

Firms are increasingly seeking consistent, comprehensive views of market activity so they can constantly assess relative value and analyze activity across instruments. Whether tracking interest rate movements in money market instruments, analyzing equity trading volumes at the security level, or monitoring activity in global credit derivatives, accurate observation of market activity plays an important role in asset allocation and performance. The most valuable datasets are those grounded in actual transactions and settlement workflows, supported by deep operational context.  

Another defining feature of today’s data landscape is the demand for timeliness, regardless of time zone. During macroeconomic or geopolitical events, market conditions can shift rapidly, reducing the value of delayed information. As a result, there is increasing emphasis on intraday and near‑real‑time datasets that allow firms to monitor developments as they unfold. For example, changes in US interest rate policy can directly affect US Treasuries and, in turn, US Treasury repo markets.  

Equally important is the ability to place current signals in historical context. Market participants rely on historical datasets to back‑test strategies, benchmark current conditions against past events and understand how markets have behaved across cycles.  

The role of data in financial markets will keep expanding. As products evolve, trading hours extend and automation increases, the demand for high‑quality market insight is expected to intensify. Firms that invest in robust data strategies, focused on transparency, timeliness and trust, will be better positioned to navigate uncertainty and remain competitive in an ever-evolving ecosystem. When used effectively, data provides the intelligence needed to improve investment performance, reduce risk and inform strategic decision‑making.