Newsletter of the Bachelier Finance Society

Volume 13, Number 3, July 2021


Due to the current situation, it has been decided to postpone the Congress again.
New dates are 13-17 June 2022.


Hans-Jürgen Engelbert (1944–2021), professor emeritus at the Friedrich Schiller University Jena (FSU), passed away on 23 May 2021. He got his diploma from FSU in 1968 and his PhD from the Lomonosov Moscow State University under the supervision of Albert Shiryaev in 1972. He was a member of the faculty at FSU ever since in various positions until his retirement in 2009.
His research interests covered numerous areas of stochastics, in particular Markovian processes, optimal stopping and control of random processes, martingale theory, stochastic differential equations, and functionals of random processes. He and his family are in our thoughts.
Adapted from the Obituary by Rainer Buckdahn, Paolo Di Tella and Wolfgang Schmidt.


The aim of these postings is to create a forum for the dissemination of information on academic and industrial positions related to mathematical finance, across different disciplines and different geographical regions. Please submit any job advertisements you are aware of to, preferably in plain text and sending the link to the website containing all the information. Updates and new items appear continuously at:

King’s College London
Deadline: July 4, 2021


BFS Summer School
The BFS summer school 2021 aims to bring advanced Master students, PhD and Postdoctoral students to the front of current research topics in financial mathematics.

Nicola Bruti Liberati Prize
We are happy to announce that the Nicola Bruti Liberati Prize 2020 has been awarded to Daniel Bartl. We congratulate the winner warmly for his achievement.

Call for Papers for “Frontiers of Mathematical Finance”
Frontiers of Mathematical Finance invites submissions of developments in the Mathematical Sciences of relevance to the field of Mathematical Finance, especially those that move the frontiers forward.


The Society maintains a list of books, book reviews and journals at: Members who would like to have their books added to the website, should please let us know.


Machine Learning in Finance

From Theory to Practice

by Matthew F. Dixon, Igor Halperin, Paul Bilokon

reviewed by Nils Detering
(UC Santa Barbara​)

I am grateful for the opportunity to review the book “Machine Learning in Finance” by M.F. Dixon, I. Halperin, and P. Bilokon. The book had been on my reading list for a couple of months, but I only started to finally dive into it recently, thanks to the approaching deadline of the newsletter.

To summarize my conclusion, I think the book is a highly valuable addition to the literature, and many readers will find it helpful. The authors mention in the introduction that their book is “targeted at graduate students in data science, mathematical finance, financial engineering and operations research seeking a career in quantitative finance,…”. I would add the researcher or professional in quantitative finance who wants to learn about machine learning and how it could be applied to questions usually addressed by our community. I am not an expert in the field of machine learning and I had only little exposure to the topic so far. My judgement is, therefore, from a non-expert perspective which is likely the situation of most readers.

I think the book provides a nice, self-contained introduction to machine learning and its applications to finance. The authors assume almost no knowledge of statistics and even basic Bayesian and frequentist statistics concepts are introduced first. It is very pleasant for someone with a financial mathematics background that basically every example or application comes from finance, which makes them feel to be on familiar grounds. I resist from summarizing the contents of the different chapters as this is nicely done at the beginning of the book, but I would like to mention that in addition to standard topics that cannot be missed in any book on machine learning, the authors also treat many non-standard aspects that are particularly relevant for the finance domain. To give an example, the question of whether a neural network is positive or convex arises naturally in the context of derivative pricing and therefore receives sufficient attention in this book.

I started my journey into the book with Chapter 9 “Introduction to Reinforcement Learning” since this chapter covers a topic unfamiliar to me. I will describe my reading experience exemplified in this chapter. All chapters follow a similar structure, and I hope that this gives a good idea about the book’s general style.

The chapter starts with a relatively long introduction section. First, the roadmap for the chapter is presented, and then the reader is introduced to reinforcement learning from an intuitive perspective. The reader learns the main terminology, the differences between on-line and off-line (batch) reinforcement learning, and the exploration-exploitation dilemma. The introduction is relatively long, but I find it useful because after reading it the reader can tell whether reinforcement learning could be used to solve a problem that they might have in mind. The next section is still in the style of an introduction and explains the main elements of reinforcement learning such as reward, value and policy functions in more detail. The evolution of the environment, feedback effects from the agents’ action, and (hidden) Markov models are discussed. The authors consider optimal stock execution and portfolio optimization as accompanying examples. Section 1 and 2 total ten pages, and some readers might find this a somewhat lengthy introduction but I assume that the authors’ goal is to reach a broad audience without assuming much prior knowledge. From this perspective, I think they made a reasonable choice. The examples are well-chosen and outlined in a way that they are accessible without prior knowledge of stochastic control and the literature on optimal execution. The next section is more technical and provides an introduction to Markov Decision Processes. The reader learns about the state and action value function for a given policy, their Bellman equations and the optimal policy, and the Bellman optimality principle. Section 4 is devoted to the dynamic programming method and its implementation. Policy and value iteration are presented and their numerical difficulties are addressed, which then leads to the reinforcement learning approach. The financial cliff walking example of household finance accompanies this section. Finally, Section 5 dives into reinforcement learning to solve the Markov Decision problems introduced earlier, and it does so in quite some detail. The section covers both on-policy algorithms based on samples produced by using an optimal policy and off-policy methods based on samples arising from possibly suboptimal policies. Temporal difference learning as well as SARSA and Q learning are explained. The authors consider learning for discrete and continuous state and action space. All methods are accompanied by examples, and many challenges arising in the implementation are addressed. Like every chapter in the book, it concludes with a summary and a set of exercises. In addition to the exercises, every section has its own multiple-choice question set that can be used to check the learners understanding. In a repository, the authors provide the Python code for most examples. The code is well documented and, therefore, a highly valuable resource if one wants to see some example implementation. I feel that I learned a lot about reinforcement learning by reading this chapter and know better now where it could be useful in my research.

In summary, this book was greatly needed and will be a valuable resource for many readers. I recommend it to anyone who wants to catch up with machine learning in finance, and I am grateful to the authors that they provide this resource. As with most new books on an emerging topic, some inconsistencies exist here and there and some parts could still be polished. But I am sure the authors will continue to improve and revise the book to make future editions even better.


This list contains the next upcoming online seminars. A full list is available at Registration is free but compulsory.

The next Online Seminar will be in September.


This list contains conferences related to mathematical finance that take place in the next six months. A full list is available at Please let us know of conferences we are not aware of and include a URL for the event.

Many conferences are cancelled around the world due to the corona-virus pandemic. Please check the respective webpages for further information.

Virtual 24th International Congress on Insurance: Mathematics and Economics
July 5–9, 2021 (UTC-05:00)

Joint European Conference On Stochastic Optimization (ECSO) and Computational
Management Science (CMS) Conference

July 7–9, 2021
Venice, Italy

31st European Conference On Operational Research
July 11–14, 2021
Athens, Greece (Hybrid format)

Advanced Risk and Portfolio Management (ARPM) Quant Bootcamp
August 16–21, 2021
New York NY, USA

6th Berlin Workshop for Young Researchers in Mathematical Finance and Stochastic Analysis
August 23–25, 2021

14th European Summer School in Financial Mathematics
August 30 – September 3, 2021
Edinburgh, UK and online

20th International Conference Credit 2021 – Compound Risk: Climate, Disaster, Finance, Pandemic
September 23–24, 2021
Venice, Italy

German Probability & Statistics Days Mannheim
September 27 – October 1, 2021

Recent Developments in Stochastics with Applications in Mathematical Physics and Finance
October 3–9, 2021
Hammamet, Tunisia

Junior female researchers in probability
October 4–6, 2021
online or if possible hybrid in Berlin, Germany


This list contains webinars related to mathematical finance. A full list is available at

SIAM Activity Group on FME Talk Series:

Machine Learning in Finance Series:

The website researchseminars offers a comprehensive list of online seminars on a variety of topics that might be of interest to you: