Hedge Fund Algo's & Strategic Data
Oct 1, 2024
The Kickoff
Happy Monday to all you market movers! Kickstart your week with a burst of insight from Tradar — take a closer look at our chat with Michael Rude (CEO of Automated Data Inc.), catch up on hedge fund algo trends over the last few years, and find your next favourite podcast for the daily commute - all in this edition.
The Compass
Here's a rundown of what you can find in this edition:
A rundown of the busy month we've had on the go.
Great insights from our chat with Michael Rude - CEO of Automated Data Inc.
Key trends in hedge funds' algorithm usage from 2022 - 2024
Round-up of the latest news to consider
How small hedge funds can adopt a smart data strategy
Something to listen to on the way to work in celebration of International Podcast Day
Quote of the week.
Insider Trading
It’s been a productive month for us at Quanted, filled with meaningful connections and progress on all fronts. Our journey began at SIPUG Day in Zurich, where we had the chance to meet and engage with key data vendors and professionals in our domain. The conference was a fertile ground for numerous opportunities for collaboration and insights from names like S&P, Bloomberg, ICE, and NVIDIA. We also took great inspiration from fellow Tenity portfolio companies, Verdant Data and Comply Taxonomy, whose presentations exemplified the innovation in their Swiss ecosystem.
Carrying the momentum forward, we headed to New York, where we met with top executives from the leading investment banks, hedge funds, and VCs. This trip also marked another major milestone for us — hosting our first in-person quarterly advisor meeting with William Mann, Debby Goan, and Brigitte Trafford.
Back at the Tenity accelerator last week, we participated in intensive strategy sessions with Jamie Reynolds and reconnected with our mentors and advisors from Pictet, UBS Next, and Interactive Brokerage.
On the product development side, we’re pleased to report strong progress. We’re in the process of onboarding 150,000 new features, and early feedback from our closed beta has been strong — signalling promising developments ahead.
As we continue to grow and innovate, we're excited to announce that our priced equity round will be opening in the next few weeks. Whether you're an angel investor or a quant with an angelic vision, we welcome you to connect with us at invest@quanted.com to explore how you can be part of our story.
The Tradewinds
Expert Exchange
We recently met with Michael Rude, the Co-Founder and CEO of Automated Data Inc., a company dedicated to making data management more accessible through its AI-enhanced, no-code platform. With a strong background in financial technology and data solutions, Michael has been instrumental in building tools that help professionals manage complex data tasks, such as entity resolution and data matching, without the need for programming skills. Prior to founding Automated Data Inc., he led buy-side trading solutions at Refinitiv (now LSEG), where he oversaw the REDI trading platform and played a key role in its integration and growth. His experience at Goldman Sachs further highlights his expertise, where he contributed significantly to enhancing trading operations. His leadership experience also extends to OPCO Advisory, where he focuses on helping companies in the capital markets navigate challenges and drive transformation. We discussed his career, the inception of Automated Data, and his vision for the future of data management.
How has the use of data in the financial industry evolved since you began your career 33 years ago, and what has been the most memorable moment for you so far?
When I started my career on the Minneapolis Grain Exchange floor in 1991, the financial world was a vastly different place. Imagine a bustling trading floor with a massive wall board displaying prices, where brokers and traders communicated through hand signals and scribbled trades on paper tickets.
Every transaction was verbally reported and manually entered into the exchange's booking system. Fast forward to today, and the landscape has transformed dramatically. Trading floors have largely vanished, replaced by electronic systems. Algorithms now control much of the order flow, with humans stepping back from moment-to-moment decision-making. This digital revolution has ushered in a new era of data-driven investing, with the financial data and analytics business growing into a $40 billion industry.
The evolution of financial technology has been nothing short of remarkable. Early movers like Renaissance Technologies leveraged enormous datasets to fuel their quantitative trading strategies, setting a trend that every firm now follows. We've seen the emergence of alternative data as investors hunger for any additional edge, and corporations are capitalising on their data assets by selling them to eager investors. It's an arms race that shows no signs of slowing down, continuously reshaping the financial landscape.
Amidst this whirlwind of change, my most memorable career moment was helping to establish REDI, a global trading platform at Goldman Sachs. Our team expanded the platform to cover nearly every exchange worldwide, supporting round-the-clock trading 5.5 days a week. It was exhilarating to play a crucial role in bridging the gap between the analog trading floors of 1991 and today's fully electronic, modern trading environment. As I reflect on my journey from those early days to the cutting edge of financial technology, I'm struck by the incredible pace of innovation in our industry and excited to see what the future holds.
Could you provide a couple of technical insights or innovations in data integration and analytics that you believe would be particularly interesting to financial professionals?
Data integration remains a significant challenge for many organisations, often requiring skilled engineers to manually understand, load, transform, and join new datasets. This labor-intensive process creates a bottleneck, limiting the scalability of data ingestion to the availability of specialised talent. As a result, many businesses struggle to keep pace with the ever-growing volume and variety of data essential for driving insights and innovation.
Enter the ADI platform, a game-changing solution that leverages the power of Large Language Models (LLMs) and machine learning to revolutionise data integration. By semantically and statistically profiling your data, ADI gains a contextual understanding that enables it to auto-generate proprietary, optimised algorithms for data matching. This innovative approach dramatically simplifies the integration process – if ADI covers the semantic types in your data, you can simply point it at your target and reference data, hit "go" and watch as it effortlessly scales your data integration capabilities beyond
traditional limitations.
What were the 3 key skills you honed during your career that have had the most positive impact on your performance and success working with data technology?
Reflecting on my career in finance and data technology, there are three key skills which have made a difference and, I believe, have contributed to my overall success:
Active listening: This has proven to be the most crucial skill. I’ve learned to attentively listen to my clients, focusing on understanding their specific challenges and pain points. This approach has consistently led to more effective and tailored solutions.
Product-oriented mindset: Maintaining a product-centric approach has been invaluable. This involves obtaining detailed requirements from stakeholders and meticulously defining both the minimum viable product and subsequent development phases. I’ve found that ensuring a clear path to demonstrating value is essential in data-driven projects.
Executional excellence: The ability to execute effectively has been a cornerstone of my success. This encompasses assembling the right team, defining clear sprint objectives and consistently delivering results. In the rapidly evolving field of data technology, the capacity to transform strategies into tangible outcomes is paramount.
These skills have been instrumental in navigating the complexities and ever evolving landscape of finance and data technology while delivering impactful solutions to clients.
How do you anticipate the adoption of new data technologies and innovations will affect the relationship between data providers and their clients?
It's amazing how the world of data is changing right before our eyes. New technologies and fresh ideas are completely reshaping how data providers and their clients work together. We're witnessing a shift from traditional file and batch-based relationships to dynamic, real-time engagements between data providers and their clients. This evolution is not just about speed; it's about creating a more interactive and responsive ecosystem that can adapt to the ever-changing needs of data consumers.
These advancements are ushering in an era of unprecedented customisation and flexibility. Clients now have the power to tailor their data feeds to specific requirements, accessing precisely the information they need, when they need it. This shift towards real- time, customisable data solutions is not only enhancing the value proposition for clients but also fostering stronger, more collaborative relationships between data providers and their customers. As a result, we're seeing increased efficiency, reduced latency, and more informed decision-making across various industries that rely on timely and accurate data.
Besides AI, what key trends do you foresee having the most significant impact on the data and analytics industry in the next 5, 10, and 25 years, and why?
Every organisation is on a journey to leverage data for decision-making and artificial intelligence applications. However, many will struggle (and likely fail) in this endeavour due to common challenges: data trapped in silos, lack of reliable identifiers for matching, and questionable data quality. As a result, often their data isn't adequately prepared to drive intelligent use cases effectively.
To truly become data-driven in a scalable and reliable way, companies need foundational technology that consistently connects data across silos, systems, and applications. We foresee organisations investing in data connectivity platforms that enable the creation of unified views of companies, people, places, and products across systems. These platforms will also facilitate the establishment of relationships both across and within these entities. This technology will be crucial in enabling companies to unlock actionable insights from their data, paving the way for more informed decision- making and successful AI implementations.
What is the next major project or initiative you’re working on, and how do you see it improving the domain your operating in?
The ADI platform leverages over forty entity resolution and data matching frameworks, enabling the connection of nearly any data type across unstructured, semi-structured, and structured data. By integrating Large Language Models (LLMs), semantic profiling, machine learning, and proprietary algorithms, we've significantly automated the entire process. This automation is crucial as it democratises data matching, making it accessible to data citizens, not just skilled engineers. Our approach unlocks the potential
for broader data utilization across organizations, empowering users at various skill levels to derive insights from complex data landscapes.
Looking ahead, our focus is on benchmarking entity resolution and data matching frameworks to better predict accuracy and demonstrate quality. This initiative aligns with our broader ambition to substantially automate data connectivity across silos, systems, and applications. Furthermore, we envision incorporating an ontological model into the platform, allowing our customers to create relationships across and within their data. This advancement will unlock the ability to generate comprehensive knowledge graphs from their data, providing a powerful tool for deeper insights and more informed decision-making. Through these enhancements, we continue to push the boundaries of what's possible in data integration and analysis.
Do you have any upcoming initiatives or projects our readers should keep an eye on?
On Thursday, October 10th, Automated Data Inc and Code Willing, will be participating in an exclusive discussion on the impact of AI and LLMs during Eagle Alpha’s Data Week NYC.
Curious about the future of data automation and generating next gen intelligence? Explore Automated Data and dive into our resource centre. From trending topics to expert advice and practical solutions our rich collection of blogs, guides and news has you covered.
Want to stay in the loop? Follow ADI on LinkedIn and join the #DataConnectivity conversation.
Numbers & Narratives
Algorithmic Trading in 2024: Key Trends in Hedge Fund Strategies
In 2024, hedge funds are increasingly shifting towards sophisticated, data-driven execution strategies. Here's a breakdown of key algorithm trends based on hedge fund responses over the last three years from findings in The Trade News' Algorithmic Trading 2024 survey report.
VWAP (75% in 2024, up from 70% in 2023) continues to be the foundation for execution, reflecting its consistency in providing reliable volume-based pricing, especially during periods of normal market liquidity.
Dark liquidity seeking remains strong at 74% as funds aim to minimize market impact by accessing hidden liquidity pools, a critical strategy in thinly traded markets or during large orders.
TWAP surged from 27% to 45%, suggesting a growing preference for time-based execution to minimize market impact, particularly in volatile or fragmented markets where timing becomes crucial.
Target Close/Auction Algorithms are up to 60%, showing the rising importance of precise end-of-day execution as hedge funds increasingly optimize their closing prices for performance metrics.
Implementation Shortfall strategies (single stock: 44%, basket: 21%) highlight the ongoing effort to minimise transaction costs, particularly for large, multi-asset portfolios.
The heat-map illustrates the evolution of hedge fund algo usage from 2022 to 2024, highlighting where strategic focus is moving based on the findings in the report.
Market Pulse
Heavy U.S. Rate Cuts
The Federal Reserve's decision to implement a 50-basis-point rate cut in September 2024 represents a key shift from its inflation-fighting stance to a focus on labor market stabilisation and economic growth. This cut comes as inflation hovers around 2.5%, approaching the Fed’s long-term 2% target. "However, the context extends beyond the U.S.; globally, central banks such as those in the U.K., Eurozone, and Canada have already reduced rates, while others like India and South Korea have held off, seemingly in anticipation of the Fed’s lead. In the U.S., market reactions were immediate but volatile, with the Dow Jones initially jumping 375 points before closing down by 103 points as investors digested the implications of future rate cuts. Historically, aggressive rate cuts have sometimes preceded economic recessions, with an average 18-month lag. This time, quants are recalibrating their models to account for rate-sensitive sectors such as small-cap equities and real estate, which could benefit from lower borrowing costs and improved liquidity. Globally, the rate differential could weaken the U.S. dollar, influencing commodities and emerging markets that depend on stable exchange rates. Ultimately, the effectiveness of this rate-cut cycle will be measured by the Fed's ability to support employment while managing inflation without triggering global instability.
Revival of M&A
The resurgence of mergers and acquisitions (M&A) in 2024 is being fueled by both sector-specific drivers and broader market conditions. In pharmaceuticals, companies are acquiring biotech firms to address patent cliffs and sustain innovation pipelines in response to slower organic revenue growth. Similarly, in technology, more firms are turning to M&A to enhance capabilities in AI, cybersecurity, and cloud services, leveraging these acquisitions to remain competitive amid rapid technological disruption. The banking sector is focusing on strategic divestitures and the acquisition of capital-light assets, such as wealth management services, as regulatory scrutiny and macroeconomic uncertainties deter large-scale consolidations. Banks are also divesting non-core businesses to strengthen their balance sheets. In fintech, companies are driving growth by expanding into digital payments through targeted acquisitions, reflecting a broader trend of capability-driven deals across industries. While overall deal volumes were down 25% in the first half of 2024 compared to 2023, the value of M&A transactions rose by 5%, particularly in megadeals within technology and energy sectors. This resurgence, supported by an expected reduction in interest rates and record levels of private equity "dry powder" totalling $2.62 trillion as of July 2024, is likely to accelerate throughout the year.
Navigational Nudges
Small hedge funds face the same challenges as large firms when it comes to managing data. Here are five ways small hedge funds can optimise their operations by adopting a smart data strategy that prioritises scalability and efficiency:
1. Focus on Critical Data Sources
Not all data is created equal.
→ Prioritise high-quality data that directly impacts your trading models and decision-making, filtering out the noise for better performance.
2. Adopt Scalable Systems
Ensure your infrastructure can grow with your fund.
→ Implement systems that scale smoothly, so you don’t need to rebuild from scratch as your data needs evolve.
3. Automate Repetitive Processes
Manual tasks can slow you down.
→ Automate testing, validation, and other repetitive processes to reduce errors and free up your team for model innovation.
4. Use Explainable Models
Transparency is key in today’s regulatory environment.
→ Explainable models, also known as white-box models, help ensure both clients and regulators understand how your models work.
5. Iterate Regularly
Markets change, and so should your models.
→ Commit to frequent updates based on fresh data to maintain model robustness and adaptability to new market conditions.
With these steps, small funds can keep infrastructure agile and processes efficient to compete with the biggest players in the industry without overextending resources.
The Knowledge Buffet
🎙 Hedge Fund Huddle by LSEG 🎙
In light of International Podcast Day today, it would be fitting to spotlight Hedge Fund Huddle, a podcast hosted by Jamie McDonald in collaboration with LSEG. Jamie, a former portfolio manager at a top hedge fund, takes an unfiltered approach to exploring the world of hedge funds.
This series offers a unique glimpse into an industry that is typically quite secretive, covering what it’s really like to work in hedge funds, along with fascinating stories of both success and failure making it an essential listen for those wanting a clearer understanding of the hedge fund world.
Whether you're commuting to work or looking for something insightful to play during a break, Hedge Fund Huddle is an excellent source of engaging content. It’s the perfect addition to your playlist, offering practical lessons and honest reflections that resonate well with anyone in the finance sector.
The Closing Bell
If you're interested in exploring investment opportunities with Quanted, we're opening our next round soon. We welcome you to reach out at invest@quanted.com if you'd like to learn more about our work and explore how you can be part of our story.
Finance Fun Corner
“At the end of World War II, the average holding period for a stock was four years. By 2000, it was eight months. By 2008, it was two months. And by 2011 it was twenty-two seconds, at least according to one professor’s estimates. One founder of a prominent high-frequency trading outfit once claimed his firm’s average holding period was a mere eleven seconds.”
- Scott Patterson, Dark Pools
The Kickoff
Happy Monday to all you market movers! Kickstart your week with a burst of insight from Tradar — take a closer look at our chat with Michael Rude (CEO of Automated Data Inc.), catch up on hedge fund algo trends over the last few years, and find your next favourite podcast for the daily commute - all in this edition.
The Compass
Here's a rundown of what you can find in this edition:
A rundown of the busy month we've had on the go.
Great insights from our chat with Michael Rude - CEO of Automated Data Inc.
Key trends in hedge funds' algorithm usage from 2022 - 2024
Round-up of the latest news to consider
How small hedge funds can adopt a smart data strategy
Something to listen to on the way to work in celebration of International Podcast Day
Quote of the week.
Insider Trading
It’s been a productive month for us at Quanted, filled with meaningful connections and progress on all fronts. Our journey began at SIPUG Day in Zurich, where we had the chance to meet and engage with key data vendors and professionals in our domain. The conference was a fertile ground for numerous opportunities for collaboration and insights from names like S&P, Bloomberg, ICE, and NVIDIA. We also took great inspiration from fellow Tenity portfolio companies, Verdant Data and Comply Taxonomy, whose presentations exemplified the innovation in their Swiss ecosystem.
Carrying the momentum forward, we headed to New York, where we met with top executives from the leading investment banks, hedge funds, and VCs. This trip also marked another major milestone for us — hosting our first in-person quarterly advisor meeting with William Mann, Debby Goan, and Brigitte Trafford.
Back at the Tenity accelerator last week, we participated in intensive strategy sessions with Jamie Reynolds and reconnected with our mentors and advisors from Pictet, UBS Next, and Interactive Brokerage.
On the product development side, we’re pleased to report strong progress. We’re in the process of onboarding 150,000 new features, and early feedback from our closed beta has been strong — signalling promising developments ahead.
As we continue to grow and innovate, we're excited to announce that our priced equity round will be opening in the next few weeks. Whether you're an angel investor or a quant with an angelic vision, we welcome you to connect with us at invest@quanted.com to explore how you can be part of our story.
The Tradewinds
Expert Exchange
We recently met with Michael Rude, the Co-Founder and CEO of Automated Data Inc., a company dedicated to making data management more accessible through its AI-enhanced, no-code platform. With a strong background in financial technology and data solutions, Michael has been instrumental in building tools that help professionals manage complex data tasks, such as entity resolution and data matching, without the need for programming skills. Prior to founding Automated Data Inc., he led buy-side trading solutions at Refinitiv (now LSEG), where he oversaw the REDI trading platform and played a key role in its integration and growth. His experience at Goldman Sachs further highlights his expertise, where he contributed significantly to enhancing trading operations. His leadership experience also extends to OPCO Advisory, where he focuses on helping companies in the capital markets navigate challenges and drive transformation. We discussed his career, the inception of Automated Data, and his vision for the future of data management.
How has the use of data in the financial industry evolved since you began your career 33 years ago, and what has been the most memorable moment for you so far?
When I started my career on the Minneapolis Grain Exchange floor in 1991, the financial world was a vastly different place. Imagine a bustling trading floor with a massive wall board displaying prices, where brokers and traders communicated through hand signals and scribbled trades on paper tickets.
Every transaction was verbally reported and manually entered into the exchange's booking system. Fast forward to today, and the landscape has transformed dramatically. Trading floors have largely vanished, replaced by electronic systems. Algorithms now control much of the order flow, with humans stepping back from moment-to-moment decision-making. This digital revolution has ushered in a new era of data-driven investing, with the financial data and analytics business growing into a $40 billion industry.
The evolution of financial technology has been nothing short of remarkable. Early movers like Renaissance Technologies leveraged enormous datasets to fuel their quantitative trading strategies, setting a trend that every firm now follows. We've seen the emergence of alternative data as investors hunger for any additional edge, and corporations are capitalising on their data assets by selling them to eager investors. It's an arms race that shows no signs of slowing down, continuously reshaping the financial landscape.
Amidst this whirlwind of change, my most memorable career moment was helping to establish REDI, a global trading platform at Goldman Sachs. Our team expanded the platform to cover nearly every exchange worldwide, supporting round-the-clock trading 5.5 days a week. It was exhilarating to play a crucial role in bridging the gap between the analog trading floors of 1991 and today's fully electronic, modern trading environment. As I reflect on my journey from those early days to the cutting edge of financial technology, I'm struck by the incredible pace of innovation in our industry and excited to see what the future holds.
Could you provide a couple of technical insights or innovations in data integration and analytics that you believe would be particularly interesting to financial professionals?
Data integration remains a significant challenge for many organisations, often requiring skilled engineers to manually understand, load, transform, and join new datasets. This labor-intensive process creates a bottleneck, limiting the scalability of data ingestion to the availability of specialised talent. As a result, many businesses struggle to keep pace with the ever-growing volume and variety of data essential for driving insights and innovation.
Enter the ADI platform, a game-changing solution that leverages the power of Large Language Models (LLMs) and machine learning to revolutionise data integration. By semantically and statistically profiling your data, ADI gains a contextual understanding that enables it to auto-generate proprietary, optimised algorithms for data matching. This innovative approach dramatically simplifies the integration process – if ADI covers the semantic types in your data, you can simply point it at your target and reference data, hit "go" and watch as it effortlessly scales your data integration capabilities beyond
traditional limitations.
What were the 3 key skills you honed during your career that have had the most positive impact on your performance and success working with data technology?
Reflecting on my career in finance and data technology, there are three key skills which have made a difference and, I believe, have contributed to my overall success:
Active listening: This has proven to be the most crucial skill. I’ve learned to attentively listen to my clients, focusing on understanding their specific challenges and pain points. This approach has consistently led to more effective and tailored solutions.
Product-oriented mindset: Maintaining a product-centric approach has been invaluable. This involves obtaining detailed requirements from stakeholders and meticulously defining both the minimum viable product and subsequent development phases. I’ve found that ensuring a clear path to demonstrating value is essential in data-driven projects.
Executional excellence: The ability to execute effectively has been a cornerstone of my success. This encompasses assembling the right team, defining clear sprint objectives and consistently delivering results. In the rapidly evolving field of data technology, the capacity to transform strategies into tangible outcomes is paramount.
These skills have been instrumental in navigating the complexities and ever evolving landscape of finance and data technology while delivering impactful solutions to clients.
How do you anticipate the adoption of new data technologies and innovations will affect the relationship between data providers and their clients?
It's amazing how the world of data is changing right before our eyes. New technologies and fresh ideas are completely reshaping how data providers and their clients work together. We're witnessing a shift from traditional file and batch-based relationships to dynamic, real-time engagements between data providers and their clients. This evolution is not just about speed; it's about creating a more interactive and responsive ecosystem that can adapt to the ever-changing needs of data consumers.
These advancements are ushering in an era of unprecedented customisation and flexibility. Clients now have the power to tailor their data feeds to specific requirements, accessing precisely the information they need, when they need it. This shift towards real- time, customisable data solutions is not only enhancing the value proposition for clients but also fostering stronger, more collaborative relationships between data providers and their customers. As a result, we're seeing increased efficiency, reduced latency, and more informed decision-making across various industries that rely on timely and accurate data.
Besides AI, what key trends do you foresee having the most significant impact on the data and analytics industry in the next 5, 10, and 25 years, and why?
Every organisation is on a journey to leverage data for decision-making and artificial intelligence applications. However, many will struggle (and likely fail) in this endeavour due to common challenges: data trapped in silos, lack of reliable identifiers for matching, and questionable data quality. As a result, often their data isn't adequately prepared to drive intelligent use cases effectively.
To truly become data-driven in a scalable and reliable way, companies need foundational technology that consistently connects data across silos, systems, and applications. We foresee organisations investing in data connectivity platforms that enable the creation of unified views of companies, people, places, and products across systems. These platforms will also facilitate the establishment of relationships both across and within these entities. This technology will be crucial in enabling companies to unlock actionable insights from their data, paving the way for more informed decision- making and successful AI implementations.
What is the next major project or initiative you’re working on, and how do you see it improving the domain your operating in?
The ADI platform leverages over forty entity resolution and data matching frameworks, enabling the connection of nearly any data type across unstructured, semi-structured, and structured data. By integrating Large Language Models (LLMs), semantic profiling, machine learning, and proprietary algorithms, we've significantly automated the entire process. This automation is crucial as it democratises data matching, making it accessible to data citizens, not just skilled engineers. Our approach unlocks the potential
for broader data utilization across organizations, empowering users at various skill levels to derive insights from complex data landscapes.
Looking ahead, our focus is on benchmarking entity resolution and data matching frameworks to better predict accuracy and demonstrate quality. This initiative aligns with our broader ambition to substantially automate data connectivity across silos, systems, and applications. Furthermore, we envision incorporating an ontological model into the platform, allowing our customers to create relationships across and within their data. This advancement will unlock the ability to generate comprehensive knowledge graphs from their data, providing a powerful tool for deeper insights and more informed decision-making. Through these enhancements, we continue to push the boundaries of what's possible in data integration and analysis.
Do you have any upcoming initiatives or projects our readers should keep an eye on?
On Thursday, October 10th, Automated Data Inc and Code Willing, will be participating in an exclusive discussion on the impact of AI and LLMs during Eagle Alpha’s Data Week NYC.
Curious about the future of data automation and generating next gen intelligence? Explore Automated Data and dive into our resource centre. From trending topics to expert advice and practical solutions our rich collection of blogs, guides and news has you covered.
Want to stay in the loop? Follow ADI on LinkedIn and join the #DataConnectivity conversation.
Numbers & Narratives
Algorithmic Trading in 2024: Key Trends in Hedge Fund Strategies
In 2024, hedge funds are increasingly shifting towards sophisticated, data-driven execution strategies. Here's a breakdown of key algorithm trends based on hedge fund responses over the last three years from findings in The Trade News' Algorithmic Trading 2024 survey report.
VWAP (75% in 2024, up from 70% in 2023) continues to be the foundation for execution, reflecting its consistency in providing reliable volume-based pricing, especially during periods of normal market liquidity.
Dark liquidity seeking remains strong at 74% as funds aim to minimize market impact by accessing hidden liquidity pools, a critical strategy in thinly traded markets or during large orders.
TWAP surged from 27% to 45%, suggesting a growing preference for time-based execution to minimize market impact, particularly in volatile or fragmented markets where timing becomes crucial.
Target Close/Auction Algorithms are up to 60%, showing the rising importance of precise end-of-day execution as hedge funds increasingly optimize their closing prices for performance metrics.
Implementation Shortfall strategies (single stock: 44%, basket: 21%) highlight the ongoing effort to minimise transaction costs, particularly for large, multi-asset portfolios.
The heat-map illustrates the evolution of hedge fund algo usage from 2022 to 2024, highlighting where strategic focus is moving based on the findings in the report.
Market Pulse
Heavy U.S. Rate Cuts
The Federal Reserve's decision to implement a 50-basis-point rate cut in September 2024 represents a key shift from its inflation-fighting stance to a focus on labor market stabilisation and economic growth. This cut comes as inflation hovers around 2.5%, approaching the Fed’s long-term 2% target. "However, the context extends beyond the U.S.; globally, central banks such as those in the U.K., Eurozone, and Canada have already reduced rates, while others like India and South Korea have held off, seemingly in anticipation of the Fed’s lead. In the U.S., market reactions were immediate but volatile, with the Dow Jones initially jumping 375 points before closing down by 103 points as investors digested the implications of future rate cuts. Historically, aggressive rate cuts have sometimes preceded economic recessions, with an average 18-month lag. This time, quants are recalibrating their models to account for rate-sensitive sectors such as small-cap equities and real estate, which could benefit from lower borrowing costs and improved liquidity. Globally, the rate differential could weaken the U.S. dollar, influencing commodities and emerging markets that depend on stable exchange rates. Ultimately, the effectiveness of this rate-cut cycle will be measured by the Fed's ability to support employment while managing inflation without triggering global instability.
Revival of M&A
The resurgence of mergers and acquisitions (M&A) in 2024 is being fueled by both sector-specific drivers and broader market conditions. In pharmaceuticals, companies are acquiring biotech firms to address patent cliffs and sustain innovation pipelines in response to slower organic revenue growth. Similarly, in technology, more firms are turning to M&A to enhance capabilities in AI, cybersecurity, and cloud services, leveraging these acquisitions to remain competitive amid rapid technological disruption. The banking sector is focusing on strategic divestitures and the acquisition of capital-light assets, such as wealth management services, as regulatory scrutiny and macroeconomic uncertainties deter large-scale consolidations. Banks are also divesting non-core businesses to strengthen their balance sheets. In fintech, companies are driving growth by expanding into digital payments through targeted acquisitions, reflecting a broader trend of capability-driven deals across industries. While overall deal volumes were down 25% in the first half of 2024 compared to 2023, the value of M&A transactions rose by 5%, particularly in megadeals within technology and energy sectors. This resurgence, supported by an expected reduction in interest rates and record levels of private equity "dry powder" totalling $2.62 trillion as of July 2024, is likely to accelerate throughout the year.
Navigational Nudges
Small hedge funds face the same challenges as large firms when it comes to managing data. Here are five ways small hedge funds can optimise their operations by adopting a smart data strategy that prioritises scalability and efficiency:
1. Focus on Critical Data Sources
Not all data is created equal.
→ Prioritise high-quality data that directly impacts your trading models and decision-making, filtering out the noise for better performance.
2. Adopt Scalable Systems
Ensure your infrastructure can grow with your fund.
→ Implement systems that scale smoothly, so you don’t need to rebuild from scratch as your data needs evolve.
3. Automate Repetitive Processes
Manual tasks can slow you down.
→ Automate testing, validation, and other repetitive processes to reduce errors and free up your team for model innovation.
4. Use Explainable Models
Transparency is key in today’s regulatory environment.
→ Explainable models, also known as white-box models, help ensure both clients and regulators understand how your models work.
5. Iterate Regularly
Markets change, and so should your models.
→ Commit to frequent updates based on fresh data to maintain model robustness and adaptability to new market conditions.
With these steps, small funds can keep infrastructure agile and processes efficient to compete with the biggest players in the industry without overextending resources.
The Knowledge Buffet
🎙 Hedge Fund Huddle by LSEG 🎙
In light of International Podcast Day today, it would be fitting to spotlight Hedge Fund Huddle, a podcast hosted by Jamie McDonald in collaboration with LSEG. Jamie, a former portfolio manager at a top hedge fund, takes an unfiltered approach to exploring the world of hedge funds.
This series offers a unique glimpse into an industry that is typically quite secretive, covering what it’s really like to work in hedge funds, along with fascinating stories of both success and failure making it an essential listen for those wanting a clearer understanding of the hedge fund world.
Whether you're commuting to work or looking for something insightful to play during a break, Hedge Fund Huddle is an excellent source of engaging content. It’s the perfect addition to your playlist, offering practical lessons and honest reflections that resonate well with anyone in the finance sector.
The Closing Bell
If you're interested in exploring investment opportunities with Quanted, we're opening our next round soon. We welcome you to reach out at invest@quanted.com if you'd like to learn more about our work and explore how you can be part of our story.
Finance Fun Corner
“At the end of World War II, the average holding period for a stock was four years. By 2000, it was eight months. By 2008, it was two months. And by 2011 it was twenty-two seconds, at least according to one professor’s estimates. One founder of a prominent high-frequency trading outfit once claimed his firm’s average holding period was a mere eleven seconds.”
- Scott Patterson, Dark Pools
The Kickoff
Happy Monday to all you market movers! Kickstart your week with a burst of insight from Tradar — take a closer look at our chat with Michael Rude (CEO of Automated Data Inc.), catch up on hedge fund algo trends over the last few years, and find your next favourite podcast for the daily commute - all in this edition.
The Compass
Here's a rundown of what you can find in this edition:
A rundown of the busy month we've had on the go.
Great insights from our chat with Michael Rude - CEO of Automated Data Inc.
Key trends in hedge funds' algorithm usage from 2022 - 2024
Round-up of the latest news to consider
How small hedge funds can adopt a smart data strategy
Something to listen to on the way to work in celebration of International Podcast Day
Quote of the week.
Insider Trading
It’s been a productive month for us at Quanted, filled with meaningful connections and progress on all fronts. Our journey began at SIPUG Day in Zurich, where we had the chance to meet and engage with key data vendors and professionals in our domain. The conference was a fertile ground for numerous opportunities for collaboration and insights from names like S&P, Bloomberg, ICE, and NVIDIA. We also took great inspiration from fellow Tenity portfolio companies, Verdant Data and Comply Taxonomy, whose presentations exemplified the innovation in their Swiss ecosystem.
Carrying the momentum forward, we headed to New York, where we met with top executives from the leading investment banks, hedge funds, and VCs. This trip also marked another major milestone for us — hosting our first in-person quarterly advisor meeting with William Mann, Debby Goan, and Brigitte Trafford.
Back at the Tenity accelerator last week, we participated in intensive strategy sessions with Jamie Reynolds and reconnected with our mentors and advisors from Pictet, UBS Next, and Interactive Brokerage.
On the product development side, we’re pleased to report strong progress. We’re in the process of onboarding 150,000 new features, and early feedback from our closed beta has been strong — signalling promising developments ahead.
As we continue to grow and innovate, we're excited to announce that our priced equity round will be opening in the next few weeks. Whether you're an angel investor or a quant with an angelic vision, we welcome you to connect with us at invest@quanted.com to explore how you can be part of our story.
The Tradewinds
Expert Exchange
We recently met with Michael Rude, the Co-Founder and CEO of Automated Data Inc., a company dedicated to making data management more accessible through its AI-enhanced, no-code platform. With a strong background in financial technology and data solutions, Michael has been instrumental in building tools that help professionals manage complex data tasks, such as entity resolution and data matching, without the need for programming skills. Prior to founding Automated Data Inc., he led buy-side trading solutions at Refinitiv (now LSEG), where he oversaw the REDI trading platform and played a key role in its integration and growth. His experience at Goldman Sachs further highlights his expertise, where he contributed significantly to enhancing trading operations. His leadership experience also extends to OPCO Advisory, where he focuses on helping companies in the capital markets navigate challenges and drive transformation. We discussed his career, the inception of Automated Data, and his vision for the future of data management.
How has the use of data in the financial industry evolved since you began your career 33 years ago, and what has been the most memorable moment for you so far?
When I started my career on the Minneapolis Grain Exchange floor in 1991, the financial world was a vastly different place. Imagine a bustling trading floor with a massive wall board displaying prices, where brokers and traders communicated through hand signals and scribbled trades on paper tickets.
Every transaction was verbally reported and manually entered into the exchange's booking system. Fast forward to today, and the landscape has transformed dramatically. Trading floors have largely vanished, replaced by electronic systems. Algorithms now control much of the order flow, with humans stepping back from moment-to-moment decision-making. This digital revolution has ushered in a new era of data-driven investing, with the financial data and analytics business growing into a $40 billion industry.
The evolution of financial technology has been nothing short of remarkable. Early movers like Renaissance Technologies leveraged enormous datasets to fuel their quantitative trading strategies, setting a trend that every firm now follows. We've seen the emergence of alternative data as investors hunger for any additional edge, and corporations are capitalising on their data assets by selling them to eager investors. It's an arms race that shows no signs of slowing down, continuously reshaping the financial landscape.
Amidst this whirlwind of change, my most memorable career moment was helping to establish REDI, a global trading platform at Goldman Sachs. Our team expanded the platform to cover nearly every exchange worldwide, supporting round-the-clock trading 5.5 days a week. It was exhilarating to play a crucial role in bridging the gap between the analog trading floors of 1991 and today's fully electronic, modern trading environment. As I reflect on my journey from those early days to the cutting edge of financial technology, I'm struck by the incredible pace of innovation in our industry and excited to see what the future holds.
Could you provide a couple of technical insights or innovations in data integration and analytics that you believe would be particularly interesting to financial professionals?
Data integration remains a significant challenge for many organisations, often requiring skilled engineers to manually understand, load, transform, and join new datasets. This labor-intensive process creates a bottleneck, limiting the scalability of data ingestion to the availability of specialised talent. As a result, many businesses struggle to keep pace with the ever-growing volume and variety of data essential for driving insights and innovation.
Enter the ADI platform, a game-changing solution that leverages the power of Large Language Models (LLMs) and machine learning to revolutionise data integration. By semantically and statistically profiling your data, ADI gains a contextual understanding that enables it to auto-generate proprietary, optimised algorithms for data matching. This innovative approach dramatically simplifies the integration process – if ADI covers the semantic types in your data, you can simply point it at your target and reference data, hit "go" and watch as it effortlessly scales your data integration capabilities beyond
traditional limitations.
What were the 3 key skills you honed during your career that have had the most positive impact on your performance and success working with data technology?
Reflecting on my career in finance and data technology, there are three key skills which have made a difference and, I believe, have contributed to my overall success:
Active listening: This has proven to be the most crucial skill. I’ve learned to attentively listen to my clients, focusing on understanding their specific challenges and pain points. This approach has consistently led to more effective and tailored solutions.
Product-oriented mindset: Maintaining a product-centric approach has been invaluable. This involves obtaining detailed requirements from stakeholders and meticulously defining both the minimum viable product and subsequent development phases. I’ve found that ensuring a clear path to demonstrating value is essential in data-driven projects.
Executional excellence: The ability to execute effectively has been a cornerstone of my success. This encompasses assembling the right team, defining clear sprint objectives and consistently delivering results. In the rapidly evolving field of data technology, the capacity to transform strategies into tangible outcomes is paramount.
These skills have been instrumental in navigating the complexities and ever evolving landscape of finance and data technology while delivering impactful solutions to clients.
How do you anticipate the adoption of new data technologies and innovations will affect the relationship between data providers and their clients?
It's amazing how the world of data is changing right before our eyes. New technologies and fresh ideas are completely reshaping how data providers and their clients work together. We're witnessing a shift from traditional file and batch-based relationships to dynamic, real-time engagements between data providers and their clients. This evolution is not just about speed; it's about creating a more interactive and responsive ecosystem that can adapt to the ever-changing needs of data consumers.
These advancements are ushering in an era of unprecedented customisation and flexibility. Clients now have the power to tailor their data feeds to specific requirements, accessing precisely the information they need, when they need it. This shift towards real- time, customisable data solutions is not only enhancing the value proposition for clients but also fostering stronger, more collaborative relationships between data providers and their customers. As a result, we're seeing increased efficiency, reduced latency, and more informed decision-making across various industries that rely on timely and accurate data.
Besides AI, what key trends do you foresee having the most significant impact on the data and analytics industry in the next 5, 10, and 25 years, and why?
Every organisation is on a journey to leverage data for decision-making and artificial intelligence applications. However, many will struggle (and likely fail) in this endeavour due to common challenges: data trapped in silos, lack of reliable identifiers for matching, and questionable data quality. As a result, often their data isn't adequately prepared to drive intelligent use cases effectively.
To truly become data-driven in a scalable and reliable way, companies need foundational technology that consistently connects data across silos, systems, and applications. We foresee organisations investing in data connectivity platforms that enable the creation of unified views of companies, people, places, and products across systems. These platforms will also facilitate the establishment of relationships both across and within these entities. This technology will be crucial in enabling companies to unlock actionable insights from their data, paving the way for more informed decision- making and successful AI implementations.
What is the next major project or initiative you’re working on, and how do you see it improving the domain your operating in?
The ADI platform leverages over forty entity resolution and data matching frameworks, enabling the connection of nearly any data type across unstructured, semi-structured, and structured data. By integrating Large Language Models (LLMs), semantic profiling, machine learning, and proprietary algorithms, we've significantly automated the entire process. This automation is crucial as it democratises data matching, making it accessible to data citizens, not just skilled engineers. Our approach unlocks the potential
for broader data utilization across organizations, empowering users at various skill levels to derive insights from complex data landscapes.
Looking ahead, our focus is on benchmarking entity resolution and data matching frameworks to better predict accuracy and demonstrate quality. This initiative aligns with our broader ambition to substantially automate data connectivity across silos, systems, and applications. Furthermore, we envision incorporating an ontological model into the platform, allowing our customers to create relationships across and within their data. This advancement will unlock the ability to generate comprehensive knowledge graphs from their data, providing a powerful tool for deeper insights and more informed decision-making. Through these enhancements, we continue to push the boundaries of what's possible in data integration and analysis.
Do you have any upcoming initiatives or projects our readers should keep an eye on?
On Thursday, October 10th, Automated Data Inc and Code Willing, will be participating in an exclusive discussion on the impact of AI and LLMs during Eagle Alpha’s Data Week NYC.
Curious about the future of data automation and generating next gen intelligence? Explore Automated Data and dive into our resource centre. From trending topics to expert advice and practical solutions our rich collection of blogs, guides and news has you covered.
Want to stay in the loop? Follow ADI on LinkedIn and join the #DataConnectivity conversation.
Numbers & Narratives
Algorithmic Trading in 2024: Key Trends in Hedge Fund Strategies
In 2024, hedge funds are increasingly shifting towards sophisticated, data-driven execution strategies. Here's a breakdown of key algorithm trends based on hedge fund responses over the last three years from findings in The Trade News' Algorithmic Trading 2024 survey report.
VWAP (75% in 2024, up from 70% in 2023) continues to be the foundation for execution, reflecting its consistency in providing reliable volume-based pricing, especially during periods of normal market liquidity.
Dark liquidity seeking remains strong at 74% as funds aim to minimize market impact by accessing hidden liquidity pools, a critical strategy in thinly traded markets or during large orders.
TWAP surged from 27% to 45%, suggesting a growing preference for time-based execution to minimize market impact, particularly in volatile or fragmented markets where timing becomes crucial.
Target Close/Auction Algorithms are up to 60%, showing the rising importance of precise end-of-day execution as hedge funds increasingly optimize their closing prices for performance metrics.
Implementation Shortfall strategies (single stock: 44%, basket: 21%) highlight the ongoing effort to minimise transaction costs, particularly for large, multi-asset portfolios.
The heat-map illustrates the evolution of hedge fund algo usage from 2022 to 2024, highlighting where strategic focus is moving based on the findings in the report.
Market Pulse
Heavy U.S. Rate Cuts
The Federal Reserve's decision to implement a 50-basis-point rate cut in September 2024 represents a key shift from its inflation-fighting stance to a focus on labor market stabilisation and economic growth. This cut comes as inflation hovers around 2.5%, approaching the Fed’s long-term 2% target. "However, the context extends beyond the U.S.; globally, central banks such as those in the U.K., Eurozone, and Canada have already reduced rates, while others like India and South Korea have held off, seemingly in anticipation of the Fed’s lead. In the U.S., market reactions were immediate but volatile, with the Dow Jones initially jumping 375 points before closing down by 103 points as investors digested the implications of future rate cuts. Historically, aggressive rate cuts have sometimes preceded economic recessions, with an average 18-month lag. This time, quants are recalibrating their models to account for rate-sensitive sectors such as small-cap equities and real estate, which could benefit from lower borrowing costs and improved liquidity. Globally, the rate differential could weaken the U.S. dollar, influencing commodities and emerging markets that depend on stable exchange rates. Ultimately, the effectiveness of this rate-cut cycle will be measured by the Fed's ability to support employment while managing inflation without triggering global instability.
Revival of M&A
The resurgence of mergers and acquisitions (M&A) in 2024 is being fueled by both sector-specific drivers and broader market conditions. In pharmaceuticals, companies are acquiring biotech firms to address patent cliffs and sustain innovation pipelines in response to slower organic revenue growth. Similarly, in technology, more firms are turning to M&A to enhance capabilities in AI, cybersecurity, and cloud services, leveraging these acquisitions to remain competitive amid rapid technological disruption. The banking sector is focusing on strategic divestitures and the acquisition of capital-light assets, such as wealth management services, as regulatory scrutiny and macroeconomic uncertainties deter large-scale consolidations. Banks are also divesting non-core businesses to strengthen their balance sheets. In fintech, companies are driving growth by expanding into digital payments through targeted acquisitions, reflecting a broader trend of capability-driven deals across industries. While overall deal volumes were down 25% in the first half of 2024 compared to 2023, the value of M&A transactions rose by 5%, particularly in megadeals within technology and energy sectors. This resurgence, supported by an expected reduction in interest rates and record levels of private equity "dry powder" totalling $2.62 trillion as of July 2024, is likely to accelerate throughout the year.
Navigational Nudges
Small hedge funds face the same challenges as large firms when it comes to managing data. Here are five ways small hedge funds can optimise their operations by adopting a smart data strategy that prioritises scalability and efficiency:
1. Focus on Critical Data Sources
Not all data is created equal.
→ Prioritise high-quality data that directly impacts your trading models and decision-making, filtering out the noise for better performance.
2. Adopt Scalable Systems
Ensure your infrastructure can grow with your fund.
→ Implement systems that scale smoothly, so you don’t need to rebuild from scratch as your data needs evolve.
3. Automate Repetitive Processes
Manual tasks can slow you down.
→ Automate testing, validation, and other repetitive processes to reduce errors and free up your team for model innovation.
4. Use Explainable Models
Transparency is key in today’s regulatory environment.
→ Explainable models, also known as white-box models, help ensure both clients and regulators understand how your models work.
5. Iterate Regularly
Markets change, and so should your models.
→ Commit to frequent updates based on fresh data to maintain model robustness and adaptability to new market conditions.
With these steps, small funds can keep infrastructure agile and processes efficient to compete with the biggest players in the industry without overextending resources.
The Knowledge Buffet
🎙 Hedge Fund Huddle by LSEG 🎙
In light of International Podcast Day today, it would be fitting to spotlight Hedge Fund Huddle, a podcast hosted by Jamie McDonald in collaboration with LSEG. Jamie, a former portfolio manager at a top hedge fund, takes an unfiltered approach to exploring the world of hedge funds.
This series offers a unique glimpse into an industry that is typically quite secretive, covering what it’s really like to work in hedge funds, along with fascinating stories of both success and failure making it an essential listen for those wanting a clearer understanding of the hedge fund world.
Whether you're commuting to work or looking for something insightful to play during a break, Hedge Fund Huddle is an excellent source of engaging content. It’s the perfect addition to your playlist, offering practical lessons and honest reflections that resonate well with anyone in the finance sector.
The Closing Bell
If you're interested in exploring investment opportunities with Quanted, we're opening our next round soon. We welcome you to reach out at invest@quanted.com if you'd like to learn more about our work and explore how you can be part of our story.
Finance Fun Corner
“At the end of World War II, the average holding period for a stock was four years. By 2000, it was eight months. By 2008, it was two months. And by 2011 it was twenty-two seconds, at least according to one professor’s estimates. One founder of a prominent high-frequency trading outfit once claimed his firm’s average holding period was a mere eleven seconds.”
- Scott Patterson, Dark Pools
