Quant Insights Conference: Portfolio Management

Professor Emanuel Derman
Financial Engineering Professor
Columbia University

Abstract Biography

A Stylized History of Quantitative Finance

The evolution of a quantitative approach to finance has proceeded through many small but significant steps and occasional large epiphanies. This talk outlines how, over the past 70 years, financial models have quantified the notion of derivatives, diffusion, risk, volatility, the riskless rate, diversification, hedging, replication, and the principle of no riskless arbitrage.

Professor Emanuel Derman, Financial Engineering Professor, Columbia University

Emanuel Derman is a professor at Columbia University, where he directs their program in financial engineering. He was born in South Africa but has lived most of his professional life in Manhattan. He started out as a theoretical physicist, doing research on unified theories of elementary particle interactions. At AT&T Bell Laboratories in the 1980s he developed programming languages for business modeling. From 1985 to 2002 he worked on Wall Street where he co-developed the Black-Derman-Toy interest rate model and the local volatility model. He is the author of 'The Volatility Smile' (Wiley, 2017), 'Models.Behaving.Badly' (Free Press 2011) one of Business Week’s top ten books of 2011. He is also the author of 'My Life As A Quant' (Wiley 2004), also one of Business Week's top ten of 2004, in which he introduced the quant world to a wide audience.

Angana Jacob
SVP Product
SigTech

Abstract Biography

Achieving Reliable Return Projections in Uncertain Times

While technology advancements and alternative data are transforming quant strategies, the models are still inevitably shaped around historical data and past behaviours. How can we construct and accurately simulate strategies that reliably profit in unprecedented economic environments?
Hear from SigTech’s VP, Quantitative Analytics, Stuart Broadfoot and Quantpedia’s CEO, Radovan Vojtko as they discuss the challenges inherent in backtesting and simulating strategies when confronted with highly uncertain markets.
Attendees will walk away from this session with:
A clear idea of the challenges seen when designing and backtesting strategies during the recent market conditions.
Some potential methods that can be applied to design robust strategies.
Thoughts on ways to identify these environments and manage allocations.

Angana Jacob, SVP Product, SigTech

Angana is SVP, Product at SigTech. Prior to joining, Angana spent more than a decade in indices and quantitative strategies at Deutsche Bank, S&P Dow Jones Indices and State Street. Her roles have spanned research, development and marketing across asset classes, as well as building out State Street's ESG analytics. Angana has a degree in Computer Science from Madras University and an MBA from IIM Ahmedabad.

Dr. Artur Sepp
Head Systematic Solutions and Portfolio Construction
Sygnum Bank

Abstract Biography

Portfolio Construction for Sector Indices of Crypto Assets

This talk will explore designing diversified exposure to crypto sectors (Web3, metaverse, DeFi, Layer2, etc), selecting portfolio weighting methodology, dealing with poor data quality, bootstrap simulation for the risk-return profile of sector indices, and the implementation of investable trackers for sector indices.

Dr. Artur Sepp, Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp is Head Systematic Solutions and Portfolio Construction at Sygnum Bank's Asset Management in Zurich, specializing in crypto assets and decentralized finance. Prior, Artur led quantitative research at a systematic hedge fund (Quantica Capital) focusing on data-driven investment strategies and asset allocation in global managed futures. In previous roles, Artur worked as front office Quant Strategist on the implementation of systematic solutions in private banking (Julius Baer), and on the full-cycle development of quantitative solutions and derivatives in investment banking (Merrill Lynch/BofA).
Artur is dedicated to connecting financial applications with science and technology. His expertise covers quantitative investing and asset allocation, modeling of financial markets and instruments, statistical and Machine Learning methods, modern computational and programming tools. His 14 years professional experience includes performing in leading roles at top quant teams in New-York, London, and Zurich.
Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for contributions to stochastic volatility and credit risk modelling with an H-index of 16. He is a member of the editorial board of the Journal of Computational Finance. Artur loves martial arts, water, and mountain sports.

Dr. Bernard Lee
Founder and CEO
HedgeSPA

Panel Abstract Biography

Factor Investing and the Road to Diversified Serfdom

Diversification, often the bedfellow of mean-variance factor-based portfolio constructed portfolio, massively favours scale for its implementation. The methodology builds around removing diversifiable and unrewarded idiosyncratic risk, defines priced systematic factors, and often their empirical proxies, as being well distributed in the cross-section. It carefully proves (to itself) that these risk factors are universal and, without barriers of information or risk-taking, are all-encompassing. Their observable representation in the empirical definition of risk factors (e.g., using Earnings Yield) is equally lazy and all-encompassing (e.g., no room for industry or size-specific measures of valuation, nor of time-series variation in the definitions due to data availability, for example).
This methodology favours the practical implementation of factor-based portfolios but does it constrain the development and addition of (i) new data-sets that are only available in parts of the cross-section and (ii) active risk-taking that is conditional on the opportunity to make alpha, versus a constant risk-taking and therefore assumptions of return harvesting?
This approach has led to the gigantic global firms that feast on scale, but take small active risk and meaningfully constrain the types of risks that managers can take. Has the pairing of diversification and scale led to an industry that sheds its fees in bloody competition, a one-way trade to the bottom, and a hollowing out of the middle section (not quite boutique, not quite scale) of the industry?
Are we witness to a virtual abandonment of meaningfully adopted portfolio innovation in the mainstream asset management industry? Has it made active managers align in the drone of uniform language (e.g., well-diversified, fundamental growth, risk-aware), and has it driven a certain subservience and feverish dependence on benchmarks as the ideal portfolios against which to compare oneself? What does it mean when we observe the active decision of portfolios has migrated, though in a lesser way, either to asset allocation or in rarer cases, to ‘unconstrained’ portfolios, where heuristic diversification rules (like equal weight) try in vain to make arguments for the mainstream?
In short, have we been defeated by our past victories and now see little ahead but the road to (diversified) serfdom? Join Michael G Kollo and panelists to find out.

Dr. Bernard Lee, Founder and CEO, HedgeSPA

Bernard Lee, Ph.D., CFA, is a recognized expert in AI and Data Analytics driven investment systems. He was previously a Managing Director at BlackRock’s Portfolio Management Group in New York, having built the multi-asset functionalities at BlackRock’s portfolio analytics platform. The analytics correctly predicted the demise of the giant hedge fund Amaranth for one of the two global asset managers that exited right in time. His work was based on intellectual properties from his Ph.D. dissertation, so he continues to have full ownership of the background intellectual properties after leaving BlackRock.
Dr. Lee is currently Founder and CEO of HedgeSPA, which is seed-funded by the Singaporean Prime Minister’s Office. The analytics platform has yielded independently verifiable track records of as much as double-digit returns without leverage across multiple global markets, and is particularly successful in the ESG space. The firm is not only active in public markets, but also offers private direct investments particularly in the sustainable energy space. It also has an impressive roster of advisors including a knighted professor as well as regional heads of global investment firms.
Dr. Lee holds a side appointment as a Professor of Practice in Finance at Hong Kong Polytechnic University. He completed his undergraduate studies at Princeton University with a double major in Engineering (Operations Research) and Woodrow Wilson School (Finance). He earned his master’s degree from the Computer Science Dept at Stanford and his Ph.D. in Quantitative Finance from Imperial College London under renowned Graph Theory expert Professor Nicos Christofides. Dr. Lee has a long list of publications at top venues including the Annual Meetings of AEA and ASA, has authored two books, and is working on his third title to be published by the printer of Nobel lectures.

Dr. Dashan Huang
Associate Professor of Finance and BNP Paribas Fellow
Lee Kong Chian School of Business

Abstract Biography

Tests of Asset Pricing Models with A Large Number of Assets

We propose both statistical and economic asset pricing tests that extend the well-known Gibbons, Ross, and Shanken (1989) test to allow for many assets whose dimensionality exceeds sample size. Empirically, we find that the tests reject six well-known asset pricing models at the stock level. In particular, the economic test provides a real time profitable trading strategy that exploits mispricing, and the mispricing patterns are similar across all the models. The significant profitability is unexplained by limits-to-arbitrage, prospect theory, and expectation extrapolation, suggesting that new factors are needed to better understand the cross section of stock returns.

Dr. Dashan Huang, Associate Professor of Finance and BNP Paribas Fellow, Lee Kong Chian School of Business

Dashan Huang is associate professor of finance and BNP Paribas Fellow at the Lee Kong Chian School of Business, Singapore Management University. He got his PhD in Finance in 2013 from Washington University in St. Louis. His research areas are asset pricing, behavioural finance, big data, and machine learning. He has published in the Review of Financial Studies, Journal of Financial Economics, Management Science, Journal of Financial and Quantitative Analysis, etc. His research has won the Emerald Best Award, Chicago Quantitative Alliance (CAQ) Academic Competition (3rd Prize), PwC 3535 Finance Forum Best Paper Award Nomination, and been synthesized by the CFA Digest.

Dmitry Zotikov, CQF
Senior Quantitative Analyst
dxFeed

Abstract Biography

Cost-Effective Composite Forex and US Equities Feeds

Currencies are typically traded OTC, which means there is no single “ground truth” foreign exchange quote stream. In this talk, we’ll discuss the problem of creating a composite FX feed that combines OTC feeds “in the best possible way” from a mathematical perspective. Expect: plots of all sorts and shapes, precalculus maths, attempted (and successful!) in-house research.

Dmitry Zotikov, Senior Quantitative Analyst, dxFeed

Dmitry Zotikov is a Senior Quantitative Analyst at dxFeed. He holds a BSc in Applied Maths and Statistics from St. Petersburg State University and a Master’s degree from Yeongnam University, where he did work on biologically realistic neural networks. His research interests are focused on applying methods of stochastic dynamics, as well as artificial neural networks for modelling market microstructure. Currently, he leads a working group that seeks to utilise advanced data analytical methods to construct market indices.
Dmitry is also a CQF alumnus.

Professor Edith Mandel
Principal
Greenwich Street Advisors LLC

Abstract Biography

Market Impact and Optimal Execution in Fixed Income: A Machine Learning Approach

We will describe the challenges of modeling execution costs in Fixed Income, and the limitations of standard approaches prevalent in execution research literature. We will then demonstrate how Machine Learning methods can help, and work through several practical applications, starting with the futures market, where prices are transparent. We will discuss how trade impact models are different from market state models, and how they can complement each other

Professor Edith Mandel, Principal, Greenwich Street Advisors LLC

As a principal at Greenwich Street Advisors, LLC, Edith provides her clients with Fixed Income quant trading know-how and signal research tools, advises them on applications of machine learning and data science in finance, and develops innovative analytics infrastructure solutions. Edith is CEO and Co-Founder of INFIO and an advisor to Proximilar LLC. Edith is a seasoned professional with over 20 years of experience in quantitative finance. Prior to starting her own firm in 2015, Edith Mandel was a Managing Director at Goldman Sachs, Citadel and the head of Fixed Income Mid-Frequency Trading at KCG (formerly GETCO).

Professor James Sefton
Professor of Economics and Director of the MSc in Investment and Wealth Management
Imperial College

Panel Abstract Biography

Factor Investing and the Road to Diversified Serfdom

Diversification, often the bedfellow of mean-variance factor-based portfolio constructed portfolio, massively favours scale for its implementation. The methodology builds around removing diversifiable and unrewarded idiosyncratic risk, defines priced systematic factors, and often their empirical proxies, as being well distributed in the cross-section. It carefully proves (to itself) that these risk factors are universal and, without barriers of information or risk-taking, are all-encompassing. Their observable representation in the empirical definition of risk factors (e.g., using Earnings Yield) is equally lazy and all-encompassing (e.g., no room for industry or size-specific measures of valuation, nor of time-series variation in the definitions due to data availability, for example).
This methodology favours the practical implementation of factor-based portfolios but does it constrain the development and addition of (i) new data-sets that are only available in parts of the cross-section and (ii) active risk-taking that is conditional on the opportunity to make alpha, versus a constant risk-taking and therefore assumptions of return harvesting?
This approach has led to the gigantic global firms that feast on scale, but take small active risk and meaningfully constrain the types of risks that managers can take. Has the pairing of diversification and scale led to an industry that sheds its fees in bloody competition, a one-way trade to the bottom, and a hollowing out of the middle section (not quite boutique, not quite scale) of the industry?
Are we witness to a virtual abandonment of meaningfully adopted portfolio innovation in the mainstream asset management industry? Has it made active managers align in the drone of uniform language (e.g., well-diversified, fundamental growth, risk-aware), and has it driven a certain subservience and feverish dependence on benchmarks as the ideal portfolios against which to compare oneself? What does it mean when we observe the active decision of portfolios has migrated, though in a lesser way, either to asset allocation or in rarer cases, to ‘unconstrained’ portfolios, where heuristic diversification rules (like equal weight) try in vain to make arguments for the mainstream?
In short, have we been defeated by our past victories and now see little ahead but the road to (diversified) serfdom? Join Michael G Kollo and panelists to find out.

Professor James Sefton, Professor of Economics and Director of the MSc in Investment and Wealth Management, Imperial College

James Sefton is Professor of Economics at Imperial College where he is Director of the MSc in Investment and Wealth Management. He has previously held senior research positions at both UBS, Winton Capital Management as well as posts at Department of Applied Economics (Cambridge University) and NIESR. He has published widely in both quantitative finance and economics more generally. His current research interests focus on quantitative investment strategies, dynamic portfolio optimisation and intergenerational equity. James is currently a member of Academic Panel of the Intergenerational Commission and UK representative of the National Transfer Accounts Project funded by UN Population Fund. He was previously advisor to the HM Treasury on their Long Term Public Finance Review and sat on the ONS Consumer Price Advisory Committee (CPAC). His publications include papers in Economic Journal, Review of Economic Studies, Financial Analyst Journal, Journal of Portfolio Management, European Economic Review, Journal of Public Economics, Journal of Economic Dynamics and Control. He was educated at Christ’s College, Cambridge from where he received both a BA and a Ph.D.

Leif Cussen
Founder
Onionfutures.com

Panel Abstract Biography

Factor Investing and the Road to Diversified Serfdom

Diversification, often the bedfellow of mean-variance factor-based portfolio constructed portfolio, massively favours scale for its implementation. The methodology builds around removing diversifiable and unrewarded idiosyncratic risk, defines priced systematic factors, and often their empirical proxies, as being well distributed in the cross-section. It carefully proves (to itself) that these risk factors are universal and, without barriers of information or risk-taking, are all-encompassing. Their observable representation in the empirical definition of risk factors (e.g., using Earnings Yield) is equally lazy and all-encompassing (e.g., no room for industry or size-specific measures of valuation, nor of time-series variation in the definitions due to data availability, for example).
This methodology favours the practical implementation of factor-based portfolios but does it constrain the development and addition of (i) new data-sets that are only available in parts of the cross-section and (ii) active risk-taking that is conditional on the opportunity to make alpha, versus a constant risk-taking and therefore assumptions of return harvesting?
This approach has led to the gigantic global firms that feast on scale, but take small active risk and meaningfully constrain the types of risks that managers can take. Has the pairing of diversification and scale led to an industry that sheds its fees in bloody competition, a one-way trade to the bottom, and a hollowing out of the middle section (not quite boutique, not quite scale) of the industry?
Are we witness to a virtual abandonment of meaningfully adopted portfolio innovation in the mainstream asset management industry? Has it made active managers align in the drone of uniform language (e.g., well-diversified, fundamental growth, risk-aware), and has it driven a certain subservience and feverish dependence on benchmarks as the ideal portfolios against which to compare oneself? What does it mean when we observe the active decision of portfolios has migrated, though in a lesser way, either to asset allocation or in rarer cases, to ‘unconstrained’ portfolios, where heuristic diversification rules (like equal weight) try in vain to make arguments for the mainstream?
In short, have we been defeated by our past victories and now see little ahead but the road to (diversified) serfdom? Join Michael G Kollo and panelists to find out.

Leif Cussen, Founder, Onionfutures.com

Leif Cussen is an experienced quantitative portfolio manager. He was the lead manager of the Merian Style Premia Absolute Return Fund. Prior to this he co-managed the Old Mutual Global Statistical Arbitrage Fund. Previously, he served as a portfolio manager in the Old Mutual quantitative strategies group, where he co-managed a range of systematic global equity market neutral and long-only funds. He was involved in a number of key product launches including the Merian Global Equity Absolute Return Fund, the team’s first market neutral UCITS fund, a European 130-30 fund, and ran a systematic ESG strategy. Leif has a BSc in computer science from Royal Holloway College, University of London, and is a CFA charterholder.

Dr. Michael G. Kollo
CEO
Clanz

Panel Abstract Biography

Factor Investing and the Road to Diversified Serfdom

Diversification, often the bedfellow of mean-variance factor-based portfolio constructed portfolio, massively favours scale for its implementation. The methodology builds around removing diversifiable and unrewarded idiosyncratic risk, defines priced systematic factors, and often their empirical proxies, as being well distributed in the cross-section. It carefully proves (to itself) that these risk factors are universal and, without barriers of information or risk-taking, are all-encompassing. Their observable representation in the empirical definition of risk factors (e.g., using Earnings Yield) is equally lazy and all-encompassing (e.g., no room for industry or size-specific measures of valuation, nor of time-series variation in the definitions due to data availability, for example).
This methodology favours the practical implementation of factor-based portfolios but does it constrain the development and addition of (i) new data-sets that are only available in parts of the cross-section and (ii) active risk-taking that is conditional on the opportunity to make alpha, versus a constant risk-taking and therefore assumptions of return harvesting?
This approach has led to the gigantic global firms that feast on scale, but take small active risk and meaningfully constrain the types of risks that managers can take. Has the pairing of diversification and scale led to an industry that sheds its fees in bloody competition, a one-way trade to the bottom, and a hollowing out of the middle section (not quite boutique, not quite scale) of the industry?
Are we witness to a virtual abandonment of meaningfully adopted portfolio innovation in the mainstream asset management industry? Has it made active managers align in the drone of uniform language (e.g., well-diversified, fundamental growth, risk-aware), and has it driven a certain subservience and feverish dependence on benchmarks as the ideal portfolios against which to compare oneself? What does it mean when we observe the active decision of portfolios has migrated, though in a lesser way, either to asset allocation or in rarer cases, to ‘unconstrained’ portfolios, where heuristic diversification rules (like equal weight) try in vain to make arguments for the mainstream?
In short, have we been defeated by our past victories and now see little ahead but the road to (diversified) serfdom? Join Michael G Kollo and panelists to find out.

Dr. Michael G. Kollo, CEO, Clanz

Dr. Michael G. Kollo is a business leader, practitioner, technical storyteller, writer and speaker. Michael gained his PhD in Finance from the London School of Economics (LSE) in the UK, before embarking on a career in quantitative asset management, working at a suite of sophisticated institutional asset managers (Blackrock, Fidelity and Axa) where he led research teams, and spoke globally on applying quantitative (data science) techniques in asset management, the limits and applications of machine learning, and the applications of alternative data for different investment problems.
Michael moved back to Australia in 2017, joining HESTA ($50bn fund) to set the quantitative finance function. During his time within the Australian asset owner community, Michael was highly sought after as a domain leader, speaker and panel presenter on the topics of deep investment research, quantitative finance, machine learning and AI in financial services.
Michael continued to engage with academic foundations, and taught Quantitative Finance at the Imperial college for three years, and fulfilling the Chief Examiner role at LSE for Corporate Finance. He is a published Author in the application of machine learning to investing, a contributor to WEF AI / Fintech panel, and a podcaster on the topic of the application of alternative data and AI/ML in Financial Services (‘Curious Quant’ podcast).
Michael is the CEO of Crypto Exchange and Asset Manager, Clanz.
Michael enjoys good intellectual banter, philosophy, psychology, and is avid sci-fi writer.

Dr. Paul Wilmott
Founder
Certificate in Quantitative Finance (CQF)

Abstract Biography

Optimal Portfolios Under the Threat of a Crash

How to allocate money between cash and a randomly moving stock is a simple, classical problem in finance. Look at Kelly and Merton for early work in that field. The simple work tends to assume simple random behaviour for the stock. But what if there is going to be a collapse in the stock price? You can look at expectations, if you have a good model for the size and probability of the crash. But what should you do in the more realistic scenario that all you know is the maximum size of the crash? You don't know anything about timing or probabilities, maybe there won't even be a crash.

Dr. Paul Wilmott, Founder, Certificate in Quantitative Finance (CQF)

Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

Priya Balan
Director, Quantitative Analytics
Barclays

Abstract Biography

Modelling Banking Book Portfolios

We discuss high level modelling approaches for forecasting balance and income of different types of banking book portfolios including key considerations for modelling different product categories. We focus on the mortgage product as an example and discuss modelling approach, potential areas of application and optimal model design to enable support for multiple uses.

Priya Balan, Director, Quantitative Analytics, Barclays

Priya is a Director in the Quantitative Analytics group in Barclays where she is head of Balance and Income Projection modelling supporting Treasury and Finance departments. She has 20 years of industry experience in a career spanning quantitative analytics, sell side research and front office technology. Prior to working in Quantitative Analytics, Priya was a strategist in the Research team at Barclays where she worked in different areas including European securitisation research, structured credit strategy and credit macro strategy. Before joining Barclays in 2007 she worked in front office technology at Deutsche Bank supporting the credit derivatives and the rates trading desks. Priya has a Masters degree in Financial Engineering from Birkbeck, University of London.

Radovan Vojtko
CEO
Quantpedia

Abstract Biography

Achieving Reliable Return Projections in Uncertain Times

While technology advancements and alternative data are transforming quant strategies, the models are still inevitably shaped around historical data and past behaviours. How can we construct and accurately simulate strategies that reliably profit in unprecedented economic environments?
Hear from SigTech’s VP, Quantitative Analytics, Stuart Broadfoot and Quantpedia’s CEO, Radovan Vojtko as they discuss the challenges inherent in backtesting and simulating strategies when confronted with highly uncertain markets.
Attendees will walk away from this session with:
A clear idea of the challenges seen when designing and backtesting strategies during the recent market conditions.
Some potential methods that can be applied to design robust strategies.
Thoughts on ways to identify these environments and manage allocations.

Radovan Vojtko, CEO, Quantpedia

Radovan Vojtko is a former Systematic Portfolio Manager, in the past, he worked for the Tatra Asset Management company (which is the biggest asset management company in the Slovak Republic and it has over 2.5 billion EURs of assets under management). He personally managed over 300+ million EUR in several quantitative funds. These funds were focused on multi-asset managed futures and trend-following strategies, global tactical asset allocation, market timing, and volatility trading. He made his next big step in 2015 and became CEO of Quantpedia.com - The Encyclopedia of Quantitative Trading Strategies, a quant research company with a mission “to turn financial academic research into a more user-friendly form to help anyone interested in algo/quant trading and systematic investing”.

Dr. Randeep Gug
Managing Director
CQF Institute

Biography

Dr. Randeep Gug, CQF Institute, Managing Director

Dr. Randeep Gug is the Managing Director of the CQF Institute and a lecturer on the Certificate in Quantitative Finance (CQF). Prior to joining Fitch Learning, Randeep worked in a variety of roles. He spent five years working in the Equities division at Salomon Smith Barney and later traded futures and options on the Indian National Stock Exchange (NSE). More recently he has spent time teaching mathematics at all levels. He is a qualified teacher, holds a 1st class honours degree and a PhD for research in semiconductor physics. He is a CQF Alumnus, achieving a distinction on the programme and his current interests are based around improving and promoting the teaching and learning of Quant Finance.

Dr. Stuart Broadfoot
VP, Quantitative Analytics
SigTech

Abstract Biography

Achieving Reliable Return Projections in Uncertain Times

While technology advancements and alternative data are transforming quant strategies, the models are still inevitably shaped around historical data and past behaviours. How can we construct and accurately simulate strategies that reliably profit in unprecedented economic environments?
Hear from SigTech’s VP, Quantitative Analytics, Stuart Broadfoot and Quantpedia’s CEO, Radovan Vojtko as they discuss the challenges inherent in backtesting and simulating strategies when confronted with highly uncertain markets.
Attendees will walk away from this session with:
A clear idea of the challenges seen when designing and backtesting strategies during the recent market conditions.
Some potential methods that can be applied to design robust strategies.
Thoughts on ways to identify these environments and manage allocations.

Dr. Stuart Broadfoot, VP, Quantitative Analytics, SigTech

Stuart is VP, Quantitative Analytics at SigTech. Prior to joining SigTech, Stuart spent more than five years at Brevan Howard Asset Management as a Quantitative Analyst and Portfolio Strategist. During this time, Stuart worked on developing quantitative analytics and researching systematic trading strategies. Stuart has a master’s degree in Mathematics from the University of Cambridge and a Doctor of Philosophy (Ph.D.) from Oxford University, in the field of Quantum Information Theory.

Quant Insights Conference:
Portfolio Management

About the Conference

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