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Magazine Issues » April 2020

Inside view: Ideal data sets for buy-side managers

Bull_marketTim Lind of the Depository Trust & Clearing Corporation (DTCC) explains how firms can access more data than ever before and describes an ideal data set for buy-side firms.

In an investment climate where passive management through index funds and ETFs continues to capture an increasing share of US investment dollars, are there new sources of data to help active management outperform market benchmarks? And as the availability of new alternative data products provide different perspectives on market activity, will asset managers embrace these new opportunities?

Essential to buy-side firms’ management strategies is having the best, most comprehensive and innovative market and reference data possible to feed their proprietary sentiment models. New data sources that can help give active managers an edge, coupled with advanced technologies and analytics designed to provide more insightful analysis, are poised to boost the capabilities of hedge funds and other buy-side firms. 

The top story around retail equity investing over the past decade has been the dramatic rise of passive management: according to Bank of America Merrill Lynch, as of May 2018, 45% of US equity assets were in passive investments, up from around 25% ten years ago.

According to Morningstar Manager Research, “Passive investment vehicles gained £19 billion ($28.8 billion) in 2019, while £32 billion was redeemed from active vehicles during the year.” 

At the same time, investment managers on the institutional side have also struggled with performance on behalf of their client base of high-net-worth individuals, large pension funds, sovereign wealth and other large funds. Bursts of market volatility may give actively managed funds a boost, since such periods have often been times for such funds to shine. During spans of relative calm, by contrast – January through November 2018, for instance – the average hedge fund lost 2%, according to research firm HFR.

While passive investing looks to remain popular for retail investors, given how it has lowered fees significantly while cost-conscious millennials become an increasing share of the investing public, the direction in active management strategies for institutional investors is to bolster the power of their quantitative and algorithmic models with data that can illuminate important trading trends.

Data supply
According to the reporting of the Wall Street Journal, 31% of all investing is now quant-driven. 

Automation, the ability to pool enormous volumes of data across multiple sources, and analytical tools like machine learning and artificial intelligence, are allowing data providers to feed today’s soaring demand for ‘big’ and ‘smart’ data in the wake of the financial crisis – a demand that’s driven in part by the wave of new, data-driven global regulations around liquidity levels and transaction reporting. 

Firms can now access aggregated market data on trading activity in key markets, by security type and at delineated time periods. They can track trading volumes by security for slices of market participants – brokers most active in specific securities, for instance. They can drill down into commercial paper and institutional certificates of deposit settlements and track position data on global credit derivatives transactions. 

This data can enable traders, portfolio managers and research analysts to identify salient trading trends and market risk indicators that may have been harder to spot in the past. 

The ability to achieve peak performance depends in no small part on the depth, breadth and quality of data an asset manager can access and utilise in its in-house trading models. With algorithms analysing data at millisecond speed, the inputs to these models are where firms can find their advantage. 

The two primary sources of such data are the in-house investment book of record, known commonly as IBOR, showing the manager’s consolidated cash and securities positions, and external trade and reference data that provide insights into market momentum, liquidity and sentiment. Given the fragmentation of US equity trading venues, consolidated sources of market information have until now been difficult to obtain and challenging to compile, but some new data offerings and tools are emerging that may help overcome these obstacles. 

An ideal data resource for buy-side asset managers and traders would be a kind of seismograph of the US equity markets revealing factors such as liquidity patterns, trends in short sales of securities, and trading concentrations across the leading broker dealers. A wishlist of information such a resource would provide might include the following:

  • Consolidated equity market summary – A broad view of market activity combining trade data from all US exchanges, alternative trading systems and dark pools – saving users from collecting and organising this voluminous information manually. To present the data in a single, standardised format would be an extra benefit.
  • Market sentiment – A view across securities’ buy, sell, sell-short, and sell-short-exempt trades. Seeing the differences in trade types over time helps users understand the market sentiment towards a stock and whether that sentiment is trending positive or negative.
  • Liquidity – Volume statistics and sub-category data to indicate how many brokers are trading large volumes of particular stocks. 

This data informs institutional investors looking to make large investments in a stock, or conversely to exit an existing position, about the number of brokers trading large enough volumes to deliver on their needs.

The return of active management
Low-cost passive investment strategies have flourished in a ten-year bull market cycle, but with a rockier investment climate potentially looming, the demand for actively managed products may start to rise. 

The power of these models can be further amplified by applying these technologies to process and analyse expansive, multi-perspective trading data like that described above. In an environment where every basis point of return is important, firms will also need to optimise their understanding of liquidity and limit the market impact of trading in and out of positions. 

Advancements in data science and AI increasingly have created sophisticated trading models that will become more pervasive; the availability of accurate and consolidated sources of data will ultimately determine if they are effective in driving new insight on the kinetics of equity markets.

Mastery of the new sources of market data and the technologies that mine and package it can become a distinguishing value proposition for buy-side firms and help data-driven active strategies make a comeback. Clients will notice and ask for more. 

Tim Lind is managing director of DTCC data services

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