Dear All,

You are cordially invited to attend the FRE Lecture Series on Thursday, November 21st in LC 400 Dibner Library – 4th Floor (5 MetroTech Center, Brooklyn, NY) at 6:00 p.m.

Dr. Ben Steiner will present a talk on the following topic:

Title:

Model Risk Management for Trading Strategies Built with Deep Learning

Abstract

Deep Learning has demonstrated spectacular success in domains outside finance and offers tantalizing potential for developing trading strategies.

This presentation reviews the basics of Deep Learning and highlights when it should (or should not) be used.

Traditionally, Model Risk Management (MRM) consists of three elements:

1. Conceptual Soundness – assessing the quality of the model design and construction;
2. Implementation Validation – confirming that the model is correctly implemented; and
3. Ongoing Monitoring – ensuring that the model is performing as intended.

In the context of trading strategies, ‘Conceptual Soundness’ can be viewed as the decision to start trading a strategy while ‘Ongoing monitoring’ is a requirement to anticipate when to stop.

Using deep learning to create trading strategies presents a number of challenges. Paramount is the non-stationary nature of financial markets: out-of-sample data is most likely drawn from a different distribution to training data. The key question is recalibration frequency: recalibrating too fast results in fitting to noise, too slowly and a model is trained on stale data. Either way, trading the sub-optimal strategy results in losses. A second challenge is interpretation. Without knowing why a strategy is performing, limited information is available for risk budgeting. The third challenge is ensuring deep learning is not simply an expensive way of rediscovering well-known factors.

In the presence of these three challenges, model risk management can still be used for evaluating deep learning trading strategies. No simple test can discriminate between good and bad strategies; rather a suite of analysis can be used to understand strategy behavior and characteristics. Ongoing monitoring is then critical to understand when live trading is not performing as intended. In this respect, evaluating deep learning strategies is an evolution of how quant trading strategies have always been evaluated. However, the increased ease with which deep learning strategies can be created now prompts even greater diligence in their systematic evaluation and ongoing monitoring.

Bio:

BNP Paribas Asset Management

In his current role, Ben handles chief-of-staff and business management responsibilities within the Global Fixed Income division of BNP Paribas Asset Management

Earlier in his career, he held roles of Head of Model Development, Portfolio Manager & Quant Researcher at investment managers and quantitative hedge funds. This experience covered models & investment strategies in multiple asset classes ranging from the traditionally illiquid (Private Debt and Real Estate) to the more liquid markets (Non-traditional Bond; Managed Futures; Global Macro and Equity Long/Short).

Prior to his current role, Ben was Head of Model Development at CIT where he managed the team researching and implementing credit models. Earlier in his career, he was a Portfolio Manager and Senior Quant Researcher at BNP and, before that, Research Manager at Aspect Capital in London. Ben started his career at Deutsche Bank in quantitative research and portfolio construction.

He holds a BA in Economics from the University of Manchester and an MSc in Mathematical Finance from Imperial College, London.
In 2013, Ben was appointed to the Board of Directors of the Society of Quantitative Analysts (SQA) and has given recent lectures on machine learning and model risk management at Columbia & NYU.

We look forward to having you join us for the talk and refreshments.

Please note that Dr. Yuexu from Fordham University will present the final lecture this semester on December 5th. Mark your calendars.