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The Thalesians

Some of our talks

Images from Thalesians events from around the world over the past 10 years

The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

Blog / See our new Thalesians blog / Book / Buy our new book, Trading Thalesians - What the ancient world can teach us about trading today (Palgrave Macmillan) by the Thalesians co-founder, Saeed Amen & foreword by founder, Paul Bilokon

Founding / The group was founded in Sep 2008, by Paul Bilokon (then a quantitative analyst at Lehman Brothers specialising in foreign exchange, and a part-time researcher at Imperial College), and two of his friends and colleagues: Matthew Dixon (then a quantitative analyst at Deutsche Bank) and Saeed Amen (then a quantitative strategist at Lehman Brothers).

The opening of Level39

The opening of Level39 in 2013 by Mayor Boris Johnson

The Thalesians are also now a member of Level39 - Europe's largest technology accelerator for finance, retail, cyber-security and future cities technology companies​

Events / Research / Consulting

Events / The Thalesians were originally based in London, UK. In Jan 2011, the organisation became truly global when Matthew Dixon brought it to the United States where he runs the Thalesians NYC seminars with New York Leader Harvey Stein. Attila Agod is the Budapest Leader for our Thalesians Budapest seminars. We are currently in the process of expanding our seminars to Prague and running more workshops.

Research / In late 2013, we started published ground breaking quant strategy notes. Our effort is lead by Saeed Amen, using nearly a decade of his experience both creating and later trading systematic trading models in FX at major investment banks. Visit Research for more.

Consulting / In 2014, we started offering bespoke quant consulting services in markets, signing up our first client, a major US hedge fund and RavenPack, a major news data vendor. Our services includes the creation of bespoke systematic trading models and other quant analysis of financial markets, such as currency hedging and FX transaction cost analysis (TCA). Visit Consulting for more.

Timeline

Our Philosophy

We are named after Thales of Miletus (Θαλῆς ὁ Μιλήσιος), a pre-Socratic Greek philosopher who lived in ca. 624 BC-ca. 546 BC. Thales was a mathematician and is familiar to many secondary school students for one of his theorems in geometry.

But more relevantly to us, he was one of the first users of options:

"Thales, so the story goes, because of his poverty was taunted with the uselessness of philosophy; but from his knowledge of astronomy he had observed while it was still winter that there was going to be a large crop of olives, so he raised a small sum of money and paid round deposits for the whole of the olive-presses in Miletus and Chios, which he hired at a low rent as nobody was running him up; and when the season arrived, there was a sudden demand for a number of presses at the same time, and by letting them out on what terms he liked he realised a large sum of money, so proving that it is easy for philosophers to be rich if they choose, but this is not what they care about."Aristotle, Politics, 1259a.

The morale of this anecdote is that it is easy for philosophers to be rich if they choose; the famous Milesian went ahead and proved it.

We, the Thalesians, admire him for that. But we also share many of his values, for example his core belief that a happy man is defined as one "ὁ τὸ μὲν σῶμα ὑγιής, τὴν δὲ ψυχὴν εὔπορος, τὴν δὲ φύσιν εὐπαίδευτος" (who is healthy in body, resourceful in soul and of a readily teachable nature).

This wiki was created to serve as a source of information on quantitative finance, to collate references to various related resources, and to serve as a convergence point for the Thalesians, our colleagues and collaborators. It grew out of Paul Bilokon's finance wiki, which he started in February, 2007.

We believe that secrecy and fidelity are important in the world of finance. But we also acknowledge the power of information sharing in open societies. Let your business logic remain a closely guarded secret. But release everything else into the public domain. What goes around, comes around; this will ultimately spare you reinventing the wheel.

Some of our talks

Some of our speakers at Thalesians events over the past decade


New York

London


Forthcoming Seminars


Thalesians Seminar (New York) — Kevin Noel — Systematic Strategies and Machine Learning

Kevin Noel

Kevin Noel

Date and Time

Wednesday, October 16 at 6:00 p.m.

Venue

Fordham University, New York, NY

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/264755069/

Abstract

Systematic strategies have a long history in the field of investment area, encompassing the high-frequency ones as well as low-frequency strategies. Over the last decade, the rise of ETF, Robo-allocator made them a popular choice compared to discretionary strategies. More recently, progresses in machine learning renew the theoretical development in that field as well as highlight new perspectives.

Here, we focus on low-frequency strategies and first recall briefly the history of such strategies through a common statistical framework (dynamic basket allocation): Markowitz, CPPI, Buy-Write, Vol. Control, Risk Budgeting, Factor-based, Arbitrage based,... We illustrate those strategies through actual use cases and highlight the importance of underlying risk framework.

In the second part, we focus on the various machine learning methods available to develop or optimize systematic strategies. Especially, we underline the paradigm difference with traditional statistical/stochastic methods by deepening on the fundamental concept of learning vs calibration, as well as the role of prior knowledge.

In the final part, we will evoke some potential future research to go beyond the paradigm of covariance matrix: neural control, graph representation learning.


Speaker

Kevin Noel is graduated from Ecole Centrale, in financial mathematics and Data mining. From 2007, He worked at BNP Paribas and then at US bank Merrill Lynch on developing advanced statistical framework and risk solutions for Institutional Investor systematic strategies in Asia/Japan. Among those solutions: volatility based, arbitrage Premium, dynamic replication of mutual/ hedge funds, long short,... Then, at ING Japan, he co-leads in Re-Insurance hedging/valuation of large scale Japanese Variable Annuities, modeling complex insurance derivatives product, as well as complex modeling of optimal end-user decision process. For the latter, he started to develop machine learning and data analytics for semi-structured, unstructured data, decided to pursue research in Machine Learning/Deep learning applied to optimality or in information processing. He joined Rakuten as Principal Data Scientist and is working on solutions for unstructured or semi-structured Big Data.

Acknowledgments

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.


Thalesians Seminar (London) — Sayad Baronyan — Fund Flows and Allocations as Predictors of Asset Returns

Sayed Baronyan

Sayad Baronyan

Date and Time

Wednesday, October 30, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/265529180/

Abstract

Fund flows and allocations data has generally been used as a discretionary input to track the overall risk appetite in the market across different asset classes, countries, or sectors. However, systematic use cases of fund flows in quantitative investment strategies have been of interest just recently. In this talk, we will explore recent research by EPFR Quantitative Research Team to better understand the quantitative use cases of fund flow and allocations data as predictors of asset returns.

Speaker

Sayad Baronyan specializes in quantitative strategies, new data product development, and research consultation. He joined Informa Financial Services, EPFR in November 2018, as a quantitative analyst. In his role, Sayad focuses on building and maintaining quantitative strategies, giving clients consultation about these strategies and authoring white papers and blog posts. Prior to Informa Financial Services, Sayad held positions at HSBC in the Multi Asset Funds Team, and Fortis as Senior Analyst. Sayad graduated with a doctorate in Finance from Özyeğin University and a MSc in Computational Science from Istanbul Technical University.


Past Seminars for 2019

Thalesians Seminar (London) — Saeed Amen — Making Python parallel with large datasets

Saeed Amen

Saeed Amen

Date and Time

Wednesday, September 25, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/264364165

Abstract

Python is a great language for data science. When working with large datasets which don't fit entirely in memory, we may need to use some different approaches. In this talk we will discuss various Python libraries which are ideal for working with large time series datasets in a pandas-like way, including dask and vaex. We shall also explore how to make computation parallel in Python, talking about the differences between threading and multiprocessing, and wrappers like concurrent.futures. We shall also talk about using the very powerful celery to distribute tasks. We shall illustrate the talk with a Jupyter notebook, including examples from finance (such as using FX tick datasets).


Speaker

Saeed Amen is the founder of Cuemacro. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor of The Book of Alternative Data (Wiley), due in 2020. Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a co-founder of the Thalesians. ---

Thalesians Seminar (New York) — Dr. Ricardo A. Collado — Time Series Forecasting With a Learning Algorithm

Dr. Ricardo A. Collado

Dr. Ricardo A. Collado

Date and Time

Tuesday, September 10, 2019, at 7:00 p.m.

Venue

Fordham University, New York, NY

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/264220071/

Abstract

We pose the problem of fitting a time series over a finite period of time as a dynamic stochastic optimization problem, in which the underlying cost functions depend on a measure of model approximation and variation in the selected parameters. We take advantage of the underlying Markov decision process to obtain a model that at optimality considers historical data as well as forecasts of future outcomes. By leveraging the theory of approximate dynamic programming we are able to obtain efficient methods that effectively react to changes in the data and consider the stream of future outcomes obtained from our past model decisions. This give rise to models calibrated to historical data which at any point in time would be optimally positioned to react to possible future data stream. We conduct a broad set of numerical experiments to test our methods on energy-related time series data. Our numerical results show our methods performing strongly against traditional time series forecast methods.


Speaker

Dr. Ricardo A. Collado currently is an Assistant Professor at the School of Business from Stevens Institute of Technology, NJ. Dr. Collado graduated from the Rutgers Center of Operations Research (RUTCOR) at Rutgers University, NJ. He has previously served as a Professional Specialist at Princeton Laboratory for Energy Systems Analysis (PENSA) from the Department of Operations Research & Financial Engineering (ORFE) at Princeton University, NJ. Dr. Collado also held a position as Assistant Professor/Faculty Fellow at Stern School of Business, Department of Information, Operations & Management Sciences at New York University, NY. His research focus on the science of decision-making in the presence of risk and utilizes dynamic stochastic optimization as its main tool. This line of research impacts areas such as finance, management science, competitive energy markets, auction theory, and homeland security. Dr. Collado's applied research program focuses in the field of energy markets problems in dynamic pricing & demand response and optimizing the design and control of energy portfolios.


Acknowledgments

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar. ---

Thalesians Seminar (London) — Abbas Edalat — Algorithmic Human Development

Abbas Edalat

Abbas Edalat

Date and Time

Wednesday, July 17, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/262652687/

Abstract

In the face of the seemingly intractable existential problems and challenges the human race currently encounters, we actually need to systematically develop protocols to enhance emotional and social intelligence and creativity in the human individual. These non-invasive protocols would be based on neuroplasticity and long term potentiation, and aim at neural retraining for optimal redevelopment and affect self-regulation in the individual regarded as a biological cybernetic system.

In simple terms, to solve our existential problems, it is time to think about protocols for human development rather than just focus on AI and making machines more computationally intelligent. Algorithmic Human Development seeks to aid individual human beings to trade their instinctual or learned traits of destructive aggression for individual and social creativity. Inspired partly by John Bowlby's Attachment Theory and supported by several computational models, our Self-Attachment protocol has parallels with Machine Learning as it employs the three basic paradigms of "substitution", "iteration" and "prior updating" in the human individual.

Edalat will give the results of a long-term pilot project on the subject and describe two Bayesian brain models for Self-Attachment: one based on Hebbian artificial neural networks and one on the Free Energy Principle. He will then describe a laughter protocol which directly counters old and entrenched beliefs of the Bayesian brain about past misfortunes and tragedies. It is designed to reduce negative emotions and boost positive affects and creativity.

Last but not least, Edalat will also finally explain how, during his detention and confinement for eight months last year, he was able to successfully extend the domain of this laughter protocol to "existing conditions" and turn a very difficult situation into a highly productive opportunity.


Speaker

Professor Abbas Edalat did his PhD in Dynamical Systems at University of Warwick supervised by Christopher Zeeman. He first took up a lectureship in Mathematics at Sharif University of Technology in Tehran and then joined the Computing Department at Imperial College London where he has been a professor of Computer Science and Mathematics since 1997.

In the past 25 years, he has developed connections between Domain Theory; a mathematical and logical theory of programming languages, with several areas of Mathematical Computation including Exact Computation, Computational Geometry, Measure and Integration Theory, Differential Calculus, Solution of ODE's, Hybrid Systems and Optimisation. Edalat has also been a Social and Political activist and researcher.

In early, 2000's he formulated the Mongol Trauma hypothesis to explain the relative demise of Islamic Societies in the Middle East as aconsequence of the enduring trans-generation of trauma caused by the Mongol invasions of the region in the 1200's. It was partly in response to this hypothesis that in 2010 he started to work on Algorithmic Human Development. ---

Thalesians Seminar (London) — Fernando de Meer — Machine learning to create synthetic financial time series

Fernando de Meer

Fernando de Meer

Date and Time

Wednesday, June 26, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/261074078

Abstract

The scarcity of historical financial data has been a huge hindrance for the development algorithmic trading models ever since the first models were devised. In the ever-changing economic reality we live in, countless models are tried and evaluated. Most of these models seek extracting information from the market by measuring a set of reasonable variables. Through backtesting, an overwhelming amount of these models are seen not to perform and are thus routinely discarded; some however do appear to work well. Out of these models, how many of their performances are just a product of overfitting? Given the lack of available historic data, this is a well-founded concern.

We want to avoid overfitting of investment strategies, and produce a rich enough environment in which to test strategies to find out the most robust.

In today’s world, this is merely wishful thinking, as we have about 55 years of high-quality equity data (or less than 700 monthly observations) for many of the metrics of US stocks we may wish to consider, much less for other types of equity.

In addition to the problem of overfitting, available data is far too small for most machine learning applications and impossibly small for advanced approaches such as deep learning or reinforcement learning. This is particularly troublesome, as Machine Learning approaches do not impose economic principles. If they work, they work in retrospect but not necessarily in the future, since the lack of interpretation and in most cases explicability.

This is where data generation comes into play. Generative Adversarial Networks, GANs, are a type of neural network architecture that have a huge potential in this regard, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds pretty similar to our own in any domain: images, music and as we'll see, financial time series.

Speaker

Fernando de Meer Pardo is currently a Junior Data Scientist at ETS Asset Management Factory and a MSc student at TU Delft, writing his MSc Thesis under the supervision of C.W.Osterlee on Financial Data Augmentation with GANs. Fernando holds as well a BSc on Mathematics from Madrid Complutense University.


Thalesians Seminar (New York) — Matthew Dixon, FRM — Blockchain Analytics for Intraday Financial Risk Modeling

Matthew Dixon

Matthew Dixon


Agenda

Tuesday, June 11, 2019:


Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023


Registration


Abstract

Blockchain provides access to the entire transaction graph yet its properties are poorly understood. One key question in this direction is the extent which the transaction graph can serve as an early-warning indicator for large financial losses. In this talk, we demonstrate the impact of extreme transaction graph activity on the intraday volatility of the Bitcoin prices series. Specifically, we introduce and characterize certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility and identify the types of chainlets that signify extreme losses. Using bars ranging from 15 minutes up to a day, we fit eGARCH models with and without the extreme chainlets and show that the former exhibit superior Value-at-Risk backtesting performance.


Speaker

Matthew Dixon, FRM, is an Assistant Professor of Applied Math at the Illinois Institute of Technology. Matthew joined the Illinois Institute of Technology in 2015 and teaches in the Masters of Mathematical Finance and Finance programs. His research in machine learning and computational methods for fintech is funded by Intel and the NSF. Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modeling. He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis respectively. He serves on the editorial board of the AIMS Journal of Dynamics and Games.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Acknowledgements

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.


Thalesians Seminar (London) — Saeed Amen — Introduction to Natural Language Processing

Saeed Amen

Saeed Amen

Date and Time

Wednesday, May 22, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/260910820

Abstract

In this talk, we shall introduce the topic of natural language processing (NLP). We shall discuss the various tasks associated with NLP, ranging from initial steps like word segmentation, to more advanced ideas like topic modelling. Later, we shall also give some specific finance use cases, such as using Twitter to help forecast payrolls, in understanding central bank communications and also with Bloomberg News to trade FX.


Speaker

Saeed Amen is the founder of Cuemacro. Over the past decade, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan). Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a co-founder of the Thalesians.


Thalesians Seminar (New York) — Paolo Guasoni — Options Portfolio Selection

Paolo Guasoni

Paolo Guasoni


Agenda

Tuesday, May 7, 2019:


Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023


Registration


Abstract

We develop a new method to optimize portfolios of options in a market where European calls and puts are available with many exercise prices for each of several potentially correlated underlying assets. We identify the combination of asset-specific option payoffs that maximizes the Sharpe ratio of the overall portfolio: such payoffs are the unique solution to a system of integral equations, which reduce to a linear matrix equation under suitable representations of the underlying probabilities. Even when implied volatilities are all higher than historical volatilities, it can be optimal to sell options on some assets while buying options on others, as hedging demand outweighs demand for asset-specific returns.


Speaker

Paolo Guasoni holds the Stokes Chair in Financial Mathematics at Dublin City University since 2009 and specializes in Mathematical Finance. His research investigates the effects of market frictions, incentives, and preferences, in portfolio choice and asset pricing, and has appeared in the Journal of Financial Economics, Finance and Stochastics, Mathematical Finance, and Annals of Applied Probability. He has attracted funding by the European Research Council, the National Science Foundation, Science Foundation Ireland, and the European Commission. He serves as Associate Editor for Finance and Stochastics, Mathematical Finance, SIAM Journal in Financial Mathematics, Applied Mathematical Finance, and the European Journal of Finance.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Acknowledgements

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.


Thalesians Seminar (London) — Blanka Horvath — Deep Learning Volatility

Blanka Horvath

Blanka Horvath

Date and Time

Wednesday, April 24, 2019, at 4:30 p.m.

Venue

King's College, Strand, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/257969639/

Abstract

We present a powerful neural network based calibration method for a number of volatility models including the rough volatility family. The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) volatility models before calibration significantly improves the generalisation performance compared to the performance of deep calibration networks that are trained directly on data.

Speaker

Blanka Horvath is a Lecturer at King's College London in the Financial Mathematics group, and an Honorary Lecturer in the Department of Mathematics at Imperial College London.

Blanka holds a PhD in Financial Mathematics from ETH Zurich, a postgraduate degree (Diplom) in Mathematics from the University of Bonn, and an MSc in Economics from The University of Hong Kong. In her research she lays a particular emphasis on the applicability of her research and maintains close collaborations with the industry, including: JP Morgan, Deutsche Bank, Zeliade Systems and AXA.

Her research interests are in the area of Stochastic Analysis and Mathematical Finance. They include (but not limited to):

  • Numerical methods as well as machine learning techniques for option pricing, forecasting and simulation.
  • Laplace methods on Wiener space and heat kernel expansions.
  • Smile asymptotics for local- and stochastic volatility models with a particular emphasis on rough volatility models and also SABR-type models.

Thalesians Seminar (New York) — Terry Benzschawel — Financial Applications of Machine Learning

Terry Benzschawel

Terry Benzschawel

Agenda

Monday, April 8, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

In this talk, I describe a variety of machine learning models that I have built and applied to problems in business and finance. I begin with an historical introduction to neural networks, including brief descriptions of the perceptron, and methods of gradient descent, backpropagation and regularization. I then describe single hidden-layer perceptrons built in the early 1990s to detect fraud on credit card portfolios, identify customers who will give up their credit cards, and later, for trading US Treasury bonds. I then describe recent work with deep learning networks that predict spread changes for corporate bonds, price moves from trade flows, and a natural language processing model that predicts market moves from sentiment data. Finally, I provide some thoughts on how artificial intelligence/machine learning is changing the fixed income trading business.


Speaker

Terry Benzschawel has recently left a thirty-year career on Wall Street to start his own firm. Prior to that, Terry was a Managing Director in Citigroup's Institutional Clients Business. Terry headed the Quantitative Credit Trading group which developed quantitative tools and strategies for credit market trading and risk management, both for Citi's clients and for in-house applications.

Terry received a Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). His Ph.D. thesis concerned development of a neural network model of the human visual system. Terry has done post-doctoral fellowships in Optometry at the University of California at Berkeley and in Ophthalmology at the Johns Hopkins University School of Medicine. He also was a visiting scientist at the IBM Thomas J. Watson Research Center prior to embarking on a career in finance. He currently serves on the steering committees of the Masters of Financial Engineering (MFE) Programs at the University of California at Berkeley and serves there as an Executive in Residence.

In 1988, Terry began his financial career at Chase Manhattan Bank, training genetic algorithms to predict corporate bankruptcy. In 1990, he was hired by Citibank to build neural network models to detect fraudulent card transactions and to predict credit card attrition. In 1992 he moved to investment banking at Salomon Brothers where he built models for proprietary trading for Salomon's Fixed Income Arbitrage Group. In 1998, he moved to the fixed income strategy as a credit strategist with a focus on client-oriented solutions across all credit markets and has worked in related roles since then. Terry was promoted to Managing Director at Citi in 2008.

Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities. In addition, he has published over a dozen articles in refereed journals and has authored two books: CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS and CREDIT MODELING: ADVANCED TOPICS. In addition, Terry has been the instructor for courses in credit modelling for Incisive Media, the Centre for Finance Professionals, the Machine Learning Institute and has taught in UCLA’s MFE program last Fall. Finally, Terry has taught a course on credit modelling at Russia's Sberbank in Moscow.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Acknowledgements

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.


Thalesians Seminar (London) — Marcos Carreira — Learning interest rate interpolation

Marcos Carreira

Marcos Careira

Date and Time

Tuesday, March 26, 2019, at 6:30 p.m.

Venue

Marriott Hotel, Canary Wharf London, UK

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/259128719/

Abstract

The usual methods for interest rate interpolation consider only the values and time to maturity of spot rates as the inputs, and differ mainly on the continuity of the implied forward rates. We treat the interpolation problem as a replication problem, where a bond (or interest rate future/swap) is priced as a function of the minimum variance replicating portfolio of the traded bonds (or derivatives). In this view, the hedging ratios determined by the interpolation are as important (if not more) than getting the “right” interpolated rate; this is similar to the adjustments to the Black and Scholes delta as a consequence of the joint dynamics of the asset price and volatility in the different volatility models. We show how to learn the parameters of the weight functions and apply this method to the overnight rate indexed interest rates derivatives in Brazil. We then extend the concept from interpolating broken dates to the market references, in order to determine which points are key to the shape and dynamics of the curve and which points can be replicated by these real anchors.

Speaker

Marcos C. S. Carreira, a PhD candidate at École Polytechnique, is the co-author of the book "Brazilian Derivatives and Securities: Pricing and Risk Management of FX and Interest-Rate Portfolios for Local and Global Markets". He was Derivative Products Officer and later Technical Modeling Officer at BM&FBovespa, where he contributed to risk management, derivatives pricing, exchange fees, microstructure and HFT functions. At Credit Suisse Brazil, he was a Managing Director in charge of the FX and IR Options desk, after being the Risk Manager responsible for Market, Counterparty and Liquidity Risks. Marcos holds an engineering degree from Instituto Tecnológico de Aeronáutica (ITA) and a Masters in Economics at Insper. Marcos also lectured for the MECAI Professional Masters course in Mathematical Finance at ICMC-USP and is a regular speaker at quantitative finance conferences.


Thalesians Seminar (New York) — Dmitriy Muravyev — Understanding Returns to Short Selling Using Option-Implied Stock Borrowing Fees

Dmitriy Muravyev

Dmitriy Muravyev

Agenda

Tuesday, March 12, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

Measures of short sale constraints and short selling activity strongly predict stock returns. This apparently exploitable predictability is difficult to explain. We partially resolve this puzzle by using measures of the stock borrowing costs paid by short-sellers. We show in portfolio sorts that the returns to short selling, net of stock borrowing costs, are much smaller than the gross returns to shorting or a typical long-short strategy. Option-implied borrowing fees, which reflect option market makers’ borrowing costs and the risks of changes in those costs, are on average only slightly higher than quoted borrowing fees. This finding indicates that the risk of changes in borrowing fee does not command a substantial risk premium. Option-implied borrowing fees predict future fees and stock returns, including returns net of quoted borrowing costs. The option-implied fee drives out other return predictors in panel regressions including option-based variables and other measures of short selling activity.


Speaker

Dmitriy Muravyev is an assistant professor of finance at Boston College’s Carroll School of Management. His research focuses on using derivative securities to answer important questions in financial economics. His recent research projects study the determinants of risk-premium and trading costs in the options market, information flows between options and the underlying stocks. His research has been published in the Journal of Financial Economics and the Journal of Finance. Professor Muravyev received his Ph.D. in Finance from the University of Illinois at Urbana-Champaign. He also holds an M.Sc. in applied mathematics from Moscow State University and an M.A. in economics from the New Economic School, Moscow.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Acknowledgements

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.


Thalesians Seminar (New York) — Yixiao (Ethan) Jiang — Semiparametric Estimation of a Credit Rating Model

Yixiao (Ethan) Jiang

Yixiao (Ethan) Jiang

Agenda

Tuesday, February 12, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

This paper develops a semiparametric, ordered-response model of credit rating in which ratings are equilibrium outcomes of a stylized cheap-talk game. The proposed model allows the assigned rating probability to be an unknown function of multiple indices permitting flexible interaction, non-monotonicity, and non-linearity in marginal effects. Based on Moody's rating data, I use the estimated model to examine credit rating agencies' (CRAs) incentive to bias ratings when the CRA's shareholders invest in bond issuers. I find the degree of Moody's rating bias varies significantly for both rating categories as well as the institutional cross-ownership between Moody's and the bond issuer. To obtain the statistical significance of these results, I prove a U-statistics equivalence result that is important for showing asymptotic normality for a large class of semiparametric models.

Speaker

Yixiao (Ethan) Jiang is currently a Ph.D. Candidate in Economics at Rutgers University, where he also completed his B.A. in Economics and Mathematics in 2013. Jiang’s research interest lies at the interface of finance and econometrics, with a current focus on estimating and testing credit risk and volatility models. His work has been presented at seminars at Vanguard, Research Affiliates, and various academic conferences, including the ASSA Annual Meeting, Financial Management Association Annual Meeting, and Econometrics Society meetings.

Jiang will join Christopher Newport University as a tenure-track Assistant Professor in August 2019.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


Thalesians Seminar (New York) — Joy Zhang — Agency MBS Prepayment Model using Neural Networks

Joy Zhang

Joy Zhang

Agenda

Tuesday, January 15, 2019:

Venue

Fordham University, McNally Amphitheatre, 140 West 62nd Street New York, NY 10023

Registration


Abstract

We apply deep neural networks, a type of machine learning method, to model agency MBS 30 year fixed rate pool prepayment behaviors. The neural networks model (“NNM”) is able to produce highly accurate model fits to the historical prepayment patterns, as well as accurate sensitivities to risk factors. These results are comparable with model results and intuition obtained from a traditional agency pool level prepayment model built via many iterations of trial and error of many months and years. This example shows NNM can process large data sets efficiently, capture very complex prepayment patterns, and can model large group of risk factors that are highly non-linear, and interactive. We also examine various potential shortcomings of this approach, including non-transparency/”blackbox” issue, model overfitting, and regime shift issues.

Speaker

Joy Zhang is an Executive Director and Head of Non-Agency Securitization Research at MSCI. Previously, Joy was a Director at Credit Suisse, responsible for mortgage collateral and regulatory modeling for securitized products trading. She also has worked as a senior developer at Goldman Sachs responsible for developing a firm-wide risk management system. Joy has an M.S. in Computational Finance from the Carnegie Mellon University and a Ph.D. in Chemistry from University of Kansas.


IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.


For older events, please see The Thalesians Quantitative Finance Seminars.

Puzzles

Masses and Buckets

You have M masses,  m_1, m_2, \ldots, m_M which you want to distribute across N buckets "as uniformly as possible". By this I mean that you are trying to minimise  \sum_{i=1}^N \sum_{j=i}^N (b_i - b_j)^2 , where bk is the sum of the masses in the k-th bucket. How would you achieve this?

To make this a little bit more concrete, suppose that I give you 20 masses, e.g. 23, 43, 12, 54, 7, 3, 5, 10, 54, 55, 26, 9, 9, 43, 54, 1, 8, 6, 38, 33. There are 4 buckets. How would you distribute the masses?

Please send your answers to paul, who happens to be at thalesians.com.

[ Solution ]