Quant Strats 2025 Blog

Alan’s responsibility is R&D for the Axioma portfolio analytics suite and his primary areas of focus are enhancing the Axioma Credit Spread Factor Model, performance attribution and liquidity capabilities on the integrated SimCorp One platform, which is designed to address the front-to-back needs of investment managers.

Machine Learning (ML) continues to revolutionise various industries, driving innovation and efficiency. Unlocking the full potential of machine learning hinges on the wisdom of industry experts who bridge theory and application.

Is the fixed income space going through an evolution, a revolution, or is it all a red herring? There is certainly enough interest in this space from some of the largest funds in the world to indicate that it’s not a red herring, but in such a vast and varying asset class, how can those unfamiliar make sense of it and more importantly, is it an asset class capable of generating consistent Alpha?

Take a look at the round-up of Quant Strats EU 2023!

In the ever-evolving landscape of artificial intelligence, Gen AI represents a groundbreaking leap forward. Building upon the foundations laid by previous AI models, Gen AI harnesses the power of deep learning to push the boundaries of what AI can achieve. In this blog post, we will delve into what's new with Gen AI and explore how its capabilities are revolutionizing various industries and aspects of our lives.

I am the Business Development Director for Computacenter’s UK’s Financial Services Sector. In 2023, this unit drove revenues approaching £1bn for the Computacenter Group. I am responsible for developing our client base, particularly in FinTech and Quant Trading. During my 22 years with the firm, helping my company to become one of the largest multi-vendor, service providers in the world. To achieve this, we have built long-term trust with our customers, our people, our partners, our communities, and our shareholders.

Mr. Jean-Marc Bonnefous is the founder and managing partner of Tellurian Capital, a London-based investment management firm focused on the digital assets and commodity sectors since 2007. He is the investment manager for the Tellurian ExoAlpha Systematic Digital Asset fund, a crypto hedge fund, in partnership with quantitative trading technology firm ExoAlpha. Prior to launching Tellurian Capital, he was an investment banker at BNP Paribas from 1992 and 2006, based in New York and London, where he built and ran the Global Commodity Derivatives business. Mr Bonnefous has also been an investor in blockchain projects since 2015. He has taught the course on ‘Blockchain & Crypto Finance’ at the IE Business School Master in Finance since 2014. 

At Glassnode, as a Senior Data Scientist, I bridge the gap between the traditional world of finance and the world of crypto. Our platform is renowned for its expertise in on-chain data, which provides a meticulous and comprehensive ledger of all blockchain transaction activities. Our exhaustive dataset is a treasure trove for quants, and my mission is to guide clients in discerning the most relevant on-chain metrics that suit their trading objectives. I focus on leveraging machine learning to extract valuable insights from On-Chain Data.

I am the Chief Investment Officer at the Multi-Family Office and Wealth Manager at Copernicus. In my role, I am overseeing the Asset Management of the firm. The company has started an exciting transformation process from a boutique shop for a few prominent families to a wealth manager that exploits this knowledge about investing for the long term for a much more comprehensive range of clients. To this end, I am building a scalable engine together with the team that allows us to grow our business in a robust and profitable way.

The world of finance is constantly evolving, and one of the most exciting developments in recent years has been the rise of quantitative credit analysis, often referred to as "quant credit." This approach to credit analysis leverages advanced mathematical models and data analytics to assess credit risk and make investment decisions. In this blog, we'll delve into the fascinating world of quant credit, exploring what it is, how it works, and why it's gaining prominence in the financial industry.

Ihsan Saracgil, senior principal data scientist at Visible Alpha, a prominent fintech firm providing data, research, and technology solutions to the buy side, sell-side, and publicly-traded companies. I lead the firm’s machine learning practice in developing new data products and publish research based on our data products.

In the realm of technology, few innovations have captured the imagination and potential as much as Natural Language Processing (NLP). With its ability to bridge the gap between human language and machine understanding, NLP has transformed the way we interact with technology and has found applications in various domains, from virtual assistants to sentiment analysis and language translation.

Cameron Brandt, Director of Research at EPFR, monitors the firm’s vast database of mutual fund and exchange-traded fund flows and positioning data across global markets. As director, Cameron mines EPFR data to isolate themes and identify relevant trends –whether a spike in leveraged bear bonds or shifts in emerging markets– to help buy– and sell-side institutions make more informed decisions. He heads a team of 10 quantitative and qualitative researchers for the database, which tracks around 130,000 share classes and represents roughly $40 trillion of assets under management.

Rob Huisman is Quantitative Researcher at Robeco’s Quant Equity Research team. His areas of expertise include portfolio optimization, machine learning, NLP, and bottom-up stock selection research. He is also responsible for recently launched Quantum strategy, part of Robeco's Next-Gen quant effort. He joined Robeco in 2018.

Dr Elliot Banks is the Chief Product Officer at BMLL. He is responsible for product development and product delivery, working closely with both clients and development teams to deliver BMLL's data and analytics to clients. Prior to that, he was Chief Data Scientist at BMLL, leading a team of 10 responsible for the data science computing environment.

The world of quantitative investing has witnessed tremendous advancements in recent years, with cutting-edge technologies transforming the landscape. Among these technologies, artificial intelligence (AI) and natural language processing (NLP) have taken center stage, with ChatGPT emerging as a game-changer. Developed by OpenAI, ChatGPT is an AI language model that holds great promise for revolutionizing the world of quantitative investing.

I am managing ML and tick data analytics products that enable quants take their research to the next level in terms of the speed of trying and validating ideas that involve tick-by-tick market data.

Natural Language Processing (NLP) has revolutionized the way we interact with technology, enabling machines to understand and generate human language. However, the development and utilization of NLP models come with their fair share of challenges. In this blog, we will explore some practical hurdles encountered during the process and delve into successful approaches to address these challenges.

In the world of data-driven decision-making, vendors often play a crucial role by providing valuable datasets to organizations. However, the process of evaluating and selecting a new dataset can be daunting for potential buyers. To overcome this challenge, vendors can adopt certain strategies to present their datasets in a manner that simplifies the decision-making process for potential customers. In this blog, we will explore successful approaches that vendors can employ to make the dataset evaluation and selection process as easy as possible for buyers.

David Murdock, CFA, is the VP of Product Management for Data Services at Visible Alpha. David is responsible for delivering Visible Alpha’s unique forecast and historical company data through various distribution channels, such as APIs, Feeds, and Cloud. In this interview he covers Visible Alpha's key priorities for the year and what he considers are the biggest challenges facing data scientists/AI experts/quantitative practitioners for 2022 and beyond.

Prior to joining Amundi, Hamza spent 12 years with Natixis Asset Management. His latest role was Head of Engineering and Quantitative Research at Seeyond, the active quantitative portfolio management arm of Natixis Investment Managers. He started his career in 2004 at AON Hewitt Associates as Financial Engineer in the Investment Consulting department. Hamza holds a Ph.D in management science (Quantitative Finance) and a Research Master Degree in Finance from the University of Paris Dauphine, and a Master Degree in Applied Statistics and Actuarial Science from CNAM-ENSAE.

Peter is the head of data science at RavenPack. Since joining RavenPack in 2008, he’s been a pioneer in the field of applied news analytics bringing alternative data insights to the world’s top banks and hedge funds. Peter has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank. He holds a Master's degree in Quantitative Finance from Sir John Cass Business School along with an undergraduate degree in Economics from Copenhagen University. Peter is a recognized speaker at quant finance conferences on alternative data and AI, and has given lectures at some of the world’s top academic institutions including London Business School, Courant Institute of Mathematics at NYU, and Imperial College London.

George Patterson is a Managing Director and the Chief Investment Officer for PGIM Quantitative Solutions. In this capacity, he oversees all portfolio management and research efforts for both the Quantitative Equity and Global Multi-Asset Solutions teams. 

Read our interview with Daniele Grassi, the CEO and Co-founder of Axyon AI as they describe the top challenges that the industry will face and what Axyon AI's top priorities are for the coming year. 

The fact that Traders Magazine recently ran an article titled “China: The Meteoric Growth of Quant Investing” is a sign of changing times. Quant strategies in emerging markets may still be behind those in the developed world. But they are catching up.

Crypto may still be a new and volatile asset class. But institutional interest is showing clear signs of awakening. The crypto market has flourished post-Covid. The fact that cryptocurrencies can be traded from anywhere in the world was a boon to potential liquidity constraints seen during the health crisis. And now, traditional hedge funds are increasingly embracing cryptocurrency investments – albeit tentatively. But they are keeping their exposure limited as the market continues to mature.

Watch our top experts in our panel - from AI & Data Science in Trading September 2021 event - as they discuss, 'Builidng and deploying ML at Scale'. Learn about organising your ML pipeline from research to deployment, the pitfalls and challenges in scaling up your ML and analytics function, and navigating the best way to organise the business to become a ML-focused organisation.

Watch our live session from AI & Data Science in Trading, September 2021, on Multi-horizon forecasting for limit order books with Stefan Zohren, Research Fellow in ML in Finance, Oxford-Man Institute. Learn about adopting techniques from machine translation to build multi-horizon forecasting models for limit order book data, achieving comparable performance to state-of-the-art algorithms on short prediction horizons, experimenting with utilizing novel hardware (IPUs) specifically designed for machine intelligence workload, and leading to significantly faster training times.

In a recent Bank of England survey, around 35% of banks reported a negative impact on Machine Learning (ML) model performance because of the pandemic. Across the finance sector, an industry which relies on understanding and mitigating risk, COVID-19 was an unprecedented shock to the system. The health crisis drove a major downturn that just could not have been forecasted based on economic data alone or historical predictors.

From AI & Data Science in Trading 2021, watch the Panel: ‘Navigating the market and remaining competitive when alternative data is ubiquitous’. Hear from the top experts including Ganesh Mani, Adjunct Faculty, Carnegie Mellon; Steve Weinstein, Chief Strategy Officer, MScience ; Sylvain Champonnois, Quantitative Researcher, CFM; Toby Dayton, CEO, Link Up. As they explore obtaining data that is accurate and relevant for optimal results and exploring the changing attitudes and strategies towards alt data after the pandemic.

It is widely recognised there are two trends gathering momentum in investing today. One is ESG and the other is the technology revolution of AI and ML: specifically the use of alternative data. Alt data is changing the wider asset management universe. This is both in the way asset managers trade and execute and in how they recruit the technological expertise they need. Today there is now more data than has been saved in the last two years than in all history.

Watch our star panel 'The Future of Alternative data - Innovative implementation of alt data in investment portfolios' from AI & Data Science in Trading from September 2021. Join the star speakers as they discuss understanding the biggest risks in investing in alternative data and where alt data has been used to drive efficiency and reduce cost and risk.

Quant Strats 5 May 2022, New York, was a huge success. We had over 200+ attendees at the event, finally networking face-to-face after 2 years! There were incredible sessions covering Quantitative Data Strategies, AI, Machine Learning, and Alternative Assets. At Quant Strats, attendees had the opportunity to learn from 40+ top experts in the industry, with our star moderators guiding the sessions. Hear from the moderators, as we talk with them about their key takeaways and more...

Watch this Keynote Panel: ESG and Sustainable Investing – Keeping up with clients’ demands from AI & Data Science in Trading, September 2021, and explore applying a quantitative approach to sustainable investing, identifying how AI and alt data is being used to identify hidden opportunities in traditional ESG data, and much more!

Watch our panel 'Leveraging Advanced ML processes to extract valuable insight and increase ROI' from AI & Data Science in Trading (now known as Quant Strats), September 2021. Learn more about rationalising the resource spend of advanced ML practices, reinforcement learning, leveraging NLP and sentiment analysis for better investment decisions.

In investment terms, ESG is now unignorable. It’s an alpha driver. Just look at the latest report by the CFA Institute, the global professional organization for investment professionals. It shows that 100% of institutional investors and 77% of retail investors are either interested in or already using ESG (Environmental, Social and Governance) strategies.

Michael Brett is SVP of Applications at Rigetti Computing and is leading the company's application development mission to connect customers and their use cases to Rigetti's quantum cloud service. 

Anthony Tassone is the Founder and CEO of GreenKey Technologies (GK), which develops natural language processing technology that structures voice and chat data for banks and trading firms. GK is based in Chicago with offices in New York and London.  

Glen High, Quantitative Analyst at Ostrum Asset Management, provides a preview to his upcoming talk at on day 2 of AI & Data Science in Trading at 1:35pm.

William Kelly, CEO, CAIA  answers our questions on the biggest challenges facing data scientists/AI experts/quantitative investors in 2019/2020 and why they are important. The Chartered Alternative Investment Analyst (CAIA) Association is the world leader in alternative investment education. 

Chris Camillo, Social Arbitrage Investor, Co-Founder/Advisor at TickerTags explains the three categories for which applications for a non-financial social mention frequency analysis generally fall into ahead of his upcoming talk at 1.35pm on Monday 16 September at AI & Data Science in Trading. 

Maurizio Garro, Senior Manager, Market, Credit & Risk at Lloyds Banking Group gives us an insight into the panel session he's part of taking place on day 2 of AI & Data Science in Trading at 4:15pm. 

This talk is about what might be called the Achilles heel of data science. It is a general talk, making reference to algorithmic trading, but applying much more generally to the applications of machine learning, AI, and statistics in the modern world of what is often called “big data”.

Separating the hype from the reality in Quantum computing is hard. More so given the highly theoretical and technical nature of the subject. How close is Quantum computing? How can you prepare for it? What impact will it have on your portfolio, risk framework, and trading strategy? What risk does it pose to your firm’s cybersecurity?

Tristen Fletcher, CEO of ChAI gives us a preview of what to expect from the upcoming panel session taking place 17 September at 9:50am. 

Over the past decade, the financial world has familiarised itself with technologies such as Artificial Intelligence (AI) and Blockchain. Today, the two are integral to services like fraud detection in banking and share trading analytics. What is the next frontier for FinTech? The vote is still out, but polls point in the direction of commodity trading.

Hugh Christensen, Founder & Head of Research at BMLL Technologies gives us a preview of what to expect from his upcoming session at AI & Data Science in Trading, taking place 16 September at 4:20pm.

L3 limit order book (LOB) data contains 30% more price discovery relevant information than L1 data. The problem is this data is difficult to work with and the information is hard to extract. While a small handful of very well resourced leading market participants have capitalized on being able to use this data over the last decade, most major financial institutions are still not able to. 

‘If you don’t go, you get left behind’

This neatly sums up the strategic imperative for the integration and use of technology in wholesale and investment banking. But which way to go? Spurred by the move to ‘open banking’ in retail, the transformation in wholesale is underway.

Alejandra Litterio, Chief Research Officer at Eye Capital, gives us an insight into unlocking the potential of NLP for Finance. Eye Capital is a technological and financial company applying Artificial Intelligence, a series of computational programs which are able to process and learn from past events simulating human being learning abilities, to create a sophisticated ecosystem of automated financial trading.

Gene Ekster - the Co-Founder of Alternative Data Group - talks us through data mapping. Alternative Data Group was started in 2017 to support the use of alternative data by institutional investors, and help their clients to develop internal, bespoke products based on raw Alternative Data.

Refinitiv's Global Head of Applied Innovation, Tim Baker, answers our questions. Tim currently leads a team of talented data and research scientists, design and u/x experts, as well as software engineers. With labs located in San Francisco, New York, London & Singapore, his team is dedicated to collectively applying new technology and innovative approaches to solve business and customer challenges.  

Since joining RavenPack in 2008, Peter Hafez has been a pioneer in the field of applied news analytics, bringing alternative data insights to the world’s top banks and hedge funds. Here he answers our questions on the prospects for the alt data market and RavenPack’s latest innovations.

Armando is an expert in applied big data and artificial intelligence technologies. He has designed systems that turn unstructured content into structured data, primarily for financial trading applications. Armando is widely regarded as one of the most knowledgeable authorities on automated text and sentiment analysis. Here he answers our questions on the prospects for the alt data market and RavenPack’s latest innovations.

With the emergence of alternative data, the alpha-landscape is currently getting seriously disrupted. The winners will be the firms that understand how to embrace and take advantage of all of the new opportunities arising from data abundance.

Rob Sloan researches and publishes a wide range of papers & articles, and hosts seminars, conferences, and briefings aimed at educating executives on cyber risk. Rob also writes a weekly column in the Wall Street Journal's popular, member-only, WSJ Pro Cybersecurity newsletter. Here he answers our questions on the AI talent war the challenges faced by organisations in recruiting and retaining the right talent.

L.D. Salmanson, Co-Founder of Cherre; a real estate data network committed to empowering all stakeholders with an interconnected data-driven platform to execute more transactions, answers our questions. 

Learn more about Consumer Edge from their founder & CEO, Bill Pecoriello, as he answers our questions. Consumer Edge is a preeminent equity research and alternative data insights boutique focused on the global consumer sector. Since its founding in 2009 CE has focused on the nexus of research and data. CE continues this focus today, delivering leading equity research with next-generation alternative data solutions. 

Co-Founder & CEO of SESAMm, Sylvain Forté answers our questions on the alt data market  and the future of AI & Data Science. SESAMm is an innovative FinTech company specializing in Big Data and Artificial Intelligence for Asset Management. They build analytics and investment signals based on 250,000 textual data sources worldwide using Natural Language Processing and specifically emotions analysis. 

Marko Djukic is the Founder and CEO of Hentsū, a technology specialist uniquely delivering public cloud-based solutions to hedge funds and asset managers. Here he answers our questions on the future of technology used to generate alpha and much more.

Crux Informatics' CEO, Philip Brittan, has thirty years of experience in the FinTech sector, both as a serial entrepreneur and as a business leader in larger firms. Here he answers our questions on the talent war of AI/ML, the introduction of cloud computing in the financial industry and the future of the alt data market.