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May 20, 2024
Alternative data: The Nirvana of rock music?
The power of going beyond traditional metrics to extract signal
in quantitative investment management remains at the forefront of
financial analytics. At our recent
S&P Global Market Intelligence Quantitative Investment
Management Forum in New York, we continued the compelling
discussions from our inaugural
London Quantitative IM Forum exploring alternative data - not
least, the lively topic of how do we define alternative data?
Alternative data = data
'Since when was Nirvana 'alternative rock'?' Seven Eight Co-founder Stephen Cash pondered on our Future of Alternative Data panel. Simply put, alternative data is just data, however for those still seeking a definition, alternative data is ultimately defined as data which 'doesn't have a ticker'. Whether defined as any data outside of standard, long-consumed sources such as financial statements and regulatory filings, or unstructured data scraped from sources such as social media, shipping or more recently synthetic data from LLMs, the consensus lay with our own Aditya Sharma's definition of alternative data as 'data not from its original asset class.'
Key to alternative data, explained Agostino Capponi, Professor
of Financial Analytics at Columbia University, is the information
it conveys, and how this flows into investment decision-making. For
example, at a macro level alternative data such as S&P Global's
Purchasing Managers' Index™ can be leveraged for nowcasting
GDP, and our
Panjiva Supply Chain Intelligence data leveraged to capture
supply chain disruption. Alternative data enables both a speed
advantage - faster processing of information, and a breadth
advantage - the ability to process more information, and to combine
a range of data types and sources rather than processing data in
isolation. 'Big Data' is now far more accessible, and in
partnership with Data Science tools to 'de-noise' data, alternative
data enables investment managers to work at a faster pace and on a
bigger scale, as well as across multiple shapes and sizes. Yuyu Fan
of Alliance Bernstein highlights 'The Four Vs of Alternative Data':
Volume, Velocity, Variety and Veracity - of which the last is most
important: data quality is fundamental for ensuring accuracy and
reliability of information.
Scale vs idiosyncrasy
Agostino Capponi underlined the importance of integrating both structured and unstructured data in an investment management process in order to maximise information extraction, and yet our Future of Alternative Data panel - led by Chris Petrescu of CP Capital - noted that alternative data can be very challenging to use, and that it is tough for alternative data to be used well. There can be challenges around managing huge databases. Conversely, there are also different risks working in smaller datasets with limited coverage and history. Investors must also be mindful of overfitting different market regimes, such as zero interest rate markets for example. And whilst alternative data is valuable for building larger portfolios of stocks, it can also be very niche, and require expertise and a lot of time to understand how value can be extracted. A dataset covering only 30 tickers must be approached idiosyncratically, but how do investment managers scale whilst being idiosyncratic? Tony Berkman, Managing Director at Two Sigma, raised this challenge and its inherent oxymoron.
In this vein, the perception of alternative data has evolved
over the last few years. Whilst many quantitative funds previously
required robust coverage, 15+ years of history and daily frequency,
most have come to appreciate that these datasets are few and far
between. As such, more firms are willing to compromise on these
criteria if they are able to see an edge in the data. Quantitative
investors typically prefer to make a lot of small bets across a
broad universe, but recognize they are potentially missing out on
alpha if they are not seeking new alternative data opportunities.
Returning to macro data, our panel considered the significance of
having some thesis around the macro-economic environment regardless
of asset class, with Ben Cohen, Head of Data Strategy at
WorldQuant, noting that it can be important to assess the impact of
macro trends that could expose value-add datasets.
Interlinking: S&P Global DNA - Data Nourishing Alpha
As highlighted earlier, investment decisions are not made using
discrete datasets - value is extracted by combining a variety of
broad data. The foundation of our S&P Global data DNA, our
S&P Global Cross Reference suite, contributes to generating
alpha by seamlessly linking multiple datasets together via a shared
identifier to organise, manage and provide structure to data, and
thus to enrich and help our customers understand the data whilst
minimizing manual processes.
Linking assets
Financial markets are becoming more interlinked, as S&P Global first explored more than a year ago whilst reviewing the relationship between the level of earnings call sentiment and changes in CDS spreads, and the 'spillage' between asset classes that our speakers considered at our London Quantitative IM Forum in Q4 2023. Furthermore, alternative data doesn't need to come from 'new' sources - traditional data with deep point in time history can be harnessed to create alternative data that adds alpha.
Over the last 12-18 months Fixed Income markets - given the
heightened environment of interest rates and inflation, and Credit
markets - where single-name CDS volumes have almost regained
pre-covid levels, have become more impactful for Equity investors,
and so - returning to our alternative data definition as 'data not
from its original asset class' - S&P Global Market Intelligence
has built a suite of cross-asset signals. Our Bond-Linked Equity Signals
enable our customers to link the Equity and Fixed Income markets by
leveraging our rich history of proprietary
CDS and
Bond Pricing data, along with our
Cross-Reference mapping. We further extend our concept of
linking by combining a broad variety of data, including our capital
flow data such as our Equity Short Interest data,
retail trade flow data and ETF compositions, as well as our
proprietary macro indicators. Returning to 'Velocity', our latest
Securities Finance research highlights how our new Intraday data is a Leading
Indicator of End-of-Day Borrows in US Equities.
Linking companies
Alternative signals can also be created from linking companies,
as demonstrated by S&P Global's new
Company Connections: Detailed Estimates product based on
traditional data - our deep and comprehensive
S&P Capital IQ Sell-Side Analyst Estimates. Research shows
that investors' inability to quickly update the asset prices of
connected companies with new value-relevant information creates an
investment opportunity. In the dynamic world of investing,
companies are not isolated entities; they are interconnected. Our
dataset and research provides a new way of
looking at these relationships through a network of shared sell
side analysts to create quantitative signals. As we look forward,
we will continue to link, leveraging our vast S&P Global data
estate to evolve our Company Connections suite with supply chain,
human capital, textual meta and additional alternative assets to
produce Equity and company alternative data signals.
Textual data
One of the key alternative data thematics for 2024 is textual
data, given the increasing adoption of AI and in particular LLMs.
As Yuyu Fan of Alliance Bernstein notes, many activities of asset
managers are driven by textual data, and many alternative data
trends require NLP capabilities to fully leverage their potential.
Most textual data is unstructured, from sources such as emails,
transcripts, articles and documents. These text files are usually
difficult, time-consuming and expensive to analyse and utilize.
S&P Global's Textual Data Suite, including our new
machine-readable Nikkei News, identifies primary sources of
textual information that can be parsed and structured for ease of
use, bypassing the entire process of sourcing, cleansing and
maintaining the data, while enabling metadata tagging and linking
to other datasets such as
financials and
estimates, on top of which we run AI and NLP analysis. Yuyu
Fan's own NLP techniques and research leverage
S&P Global's Machine Readable Transcripts to extract
insights for alpha generation, the resulting benefits of which are
impactful proprietary investment signals leveraged across equity
and fixed income strategies, as well as timely alerts sent directly
to PMs and analysts. In addition, Yuyu's work leveraging
S&P Global's Machine-Readable Filings asks the investment
question as to whether changes in 10-Ks can help to identify risk,
concluding that companies with low similarity scores
between sequential filings significantly
underperform.
AI
Leveraging technology and analytics is key for extracting
actionable insights from data, and a core transformation right now
is moving to a cloud-friendly, AI-ready view. As Michael Hoffmann
of Kensho Technologies - S&P
Global's AI hub - highlights, AI is a disruptive force for data.
From human-readability, to machine-readability, and now
AI-readability, data needs to be structured and pre-processed with
AI use cases in mind. 'AI-ready data' is thus the new age genre
taking traditional data, interconnectedness and delivery to a new
level pitch optimized for LLMs. For tabular data, this increasingly
looks like specialized LLM-ready APIs. Although LLMs must also be
able to interpret unstructured, textual data, existing dense-vector
search methods leave much to be desired. A robust design pattern
for AI-ready textual data will likely take a longer time to mature.
Yuyu Fan of Alliance Bernstein highlights the importance of
pre-training LLMs, using vast corpora of text and in local
languages, as well as providing context and well-defined prompts
for problems/questions in order to improve the quality of results,
concluding that competitive advantages can be created with expert
annotations (human feedback) and fine-tuning. However the challenge
with Generative AI is
accuracy, and as such verifiability and auditability of data are
important - 'Veracity' remains key.
Data democratised
In conclusion, the power of data and alternative data will continue to gain strength - especially as more firms adopt AI technology as mainstream, which will increasingly 'democratise' data. As more market participants gain access to AI tools, we will see Fundamental and Systematic strategies coming closer together - we already observe more quantamental approaches, quantitative strategies increasingly being applied to other asset classes such as Credit and derivatives, and also quantitative firms building discretionary teams to help inform them more about the data they are using. In future, how will firms monetise their proprietary data? As Tony Berkman, Managing Director at Two Sigma highlighted, proprietary data may soon be a line item listed on balance sheets, which could even for example be used as collateral against a loan. Meanwhile, many companies don't yet realise that they own valuable data. With ever more crowding, investment managers will need to be increasingly creative with how they leverage data, analytics and technology in order to squeeze alpha.
For all the rock music analogies, it is clear that alternative
data will never be a case of never mind.
Please feel free to download a PDF version of this
blog.
S&P Global provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.
This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.
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