Tag Archives: FinTech

Seminar

FinTech Seminar Series

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FinTech Applications of AI & Machine Learning in B2B space

With its formidable datasets within domain sets like credit default, economic, security ratings, firmographic & people data, Moody’s is well positioned to develop new products to serve the needs of its clients. The Moody’s Analytics Accelerator group aims to identify, research and develop new business products for new clients and new markets using sophisticated AI & Machine learning techniques that are now ubiquitous in the Consumer space. This talk will focus on how we have brought to market products in the Compliance, Loan Origination, Financial Spreading and Commercial Real Estate domains.

Bio

Rakesh Parameshwar is a Senior Director for Strategy & Innovation in the Moody’s Analytics Accelerator (MAA) group. The MAA group aims to identify, research, and develop new business opportunities, with a special focus on emerging technologies like AI & ML. Before joining Moody’s Analytics in 2018, he was in charge of business strategy and product development for Bloomberg’s Desktop API, Alerts and Quant Solutions group. Prior to this, he had extensive experience working for global banks where he advised clients with risk management solutions and built technology solutions. Rakesh has an MBA from NYU Stern, a Bachelor’s degree in Electrical Engineering from IIT Kharagpur in India and a certificate in Quantitative Finance (CQF)

When

Wednesday January 29, 2020
6 PM – 7PM

Where

University of Waterloo @ Manhattan Institute of Management
2 Washington Street, 17th floor
New York, New York

Seminar

October 16, 2019: IAQF/Thalesians – Systematic Strategies and Machine Learning

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IAQF Upcoming Event

Systematic Strategies and Machine Learning

 

Kevin Noel

A Talk by
Kevin Noel

Wednesday, October 16

5:45 PM Registration
6:00 PM Seminar Begins
7:30 PM Reception

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.

Biography

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.

About the Series
The IAQF’s Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com). 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.

Registration Fees:

Complimentary for IAQF members
Login and Register

Thalesians Members can register for $25

Non-Members: $25.00 by registering

Seminar

NYU FRE Lecture Series: Mykhaylo Shkolnikov

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NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the NYU FRE Lecture Series on Thursday, October 10th in the Dibner Auditorium
(5 MetroTech Center – 1st Floor) at 6 p.m.

Dr. Mykhaylo Shkolnikov will present a talk on the following topic:

Title:

From Systemic Risk to Supercooling and Back

Abstract:

I will explain how structural models of default cascades in the systemic risk literature naturally lead to the supercooled Stefan problem of mathematical physics. On the one hand, this connection allows us to uncover a notion of global solutions to the supercooled Stefan problem, which we analyze in detail. On the other hand, the supercooled Stefan problem formulation allows to provide a truly intrinsic definition of systemic crises and to characterize the fragile states of the economy. Time permitting, I will also explain the network and game extensions of the problem. Based on a series of works with Francois Delarue and Sergey Nadtochiy.

Bio:

Mykhaylo (Misha) Shkolnikov is currently an assistant professor in the Department of Operations Research and Financial Engineering, an affiliated faculty member with the Bendheim Center for Finance, and an associated faculty member with the Program in Applied & Computational Mathematics (PACM) at Princeton University. Prior to that, he was an assistant professor in the Department of Mathematics at Princeton and a postdoctoral fellow at the University of California, Berkeley and MSRI. He earned his PhD in mathematics at Stanford University. Shkolnikov is the recipient of the 2018 Erlang Prize from the Applied Probability Society of INFORMS and of the 2019 Early Career Prize from the SIAM Activity Group on Financial Mathematics and Engineering. His research focuses on stochastic portfolio theory (and, more generally, optimal investment), interacting particle systems, random matrix theory, as well as probabilistic approaches to partial differential equations.

We look forward to having you join us for the talk and refreshments. Please mark your calendars.

Seminar

October 8, 2019: Cornell-Citi Financial Data Science Seminars

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Featuring Machine Learning experts from Cornell, Citi, and more…

***For those of you who missed Tuesday night’s seminar and wish to see Dr. Miquel Noguer i Alonso’s presentation, the recording is now available.

You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Seminars at the Bloomberg Center at Cornell Tech, Room 061/071. Through the talks this semester, we are excited to collaborate with Citi in highlighting machine learning applications in finance.

2 West Loop Road
New York, NY 10044

All seminars are from 6:10 pm to 7:25 pm. This seminar will NOT be recorded.

Seminars are free. However, registration is required for NYC attendees as seating is limited.

 

Date: Tuesday, October 8, 2019
Time: 6:10pm – 7:25pm
Speaker: Puneet Singhvi | Citi
Title: “What’s Happening with Blockchain in Financial Markets?”

Abstract

Over the past few years, the financial industry has been actively exploring blockchain and distributed ledger technology (DLT) to assess their impact in various use-cases, identify benefits, and separate the hype from reality. Citi has been an active participant and strategic investor in blockchain initiatives across the ecosystem for nearly 5 years now.

In this presentation, we will discuss real use-cases in active implementation across the financial ecosystem and review key drivers for adoption. These emerging use-cases span product lines and geographies – from the digitization of post-trade activities to transformed market exchanges, and from digitized securities to cash-on-chain models, from collateral mobility to trade finance – across North America, Europe, and Asia. We will discuss areas with tangible benefits, and what have been learnings from failed initiatives. We will also review key emerging issues with the technology and potential areas of opportunity going forward.

Speaker Bio

Puneet is Managing Director and Financial Markets Infrastructure (FMI) head for Citi Institutional Client Group. He is responsible for relationship and key initiatives with FMIs such as Exchanges, Payment Systems, Clearing Houses, and Settlement venues. He also leads Blockchain/DLT and Digital Assets initiatives for the Markets and Securities Services business working actively with FMIs, FinTechs and institutional clients on identifying and delivering solutions.

Puneet has worked at Citi across the developed and emerging markets in various management roles within Citi Markets & Securities Services and Citi Trade & Transaction Services businesses. His roles included leading Citi Global Clearing Payments Product, Citi Foreign Exchange & Derivative Clearing Product Management, Trade Finance and Asset Backed Finance.

He has a Bachelor’s degree in Electronics and Communications Engineering and has completed his post-graduation in management from the Indian Institute of Management.

We hope to see you there!

The Cornell-Citi Team

Directions to CFEM & Citi @CornellTech on Roosevelt Island: Take the Tram or the F train to Roosevelt Island; walk to the left along the East River until you see a modern, bronze building, which is the Bloomberg Center. Check in at the front desk and go downstairs to the basement, where Room 061/071 will be straight ahead on your left.

**Please excuse any duplication of this announcement

Past CFEM Events

September 24, 2019
Speaker: Dr. Miquel Noguer I Alonso I Artificial Intelligence Finance Institute

Title: “Latest Developments in Deep Learning in Finance”

November 5, 2019
Speaker: Adam Grealish (Betterment)
Title: TBD

November 12, 2019
Quant Finance Forum

Seminar

NYU FRE Lecture Series: Matthias Heymann

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NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the NYU FRE Lecture Series on Thursday, October 3rd in the Event MakerSpace
(6 MetroTech Center – 1st Floor) at 6 p.m.

Dr. Matthias Heymann will present a talk on the following topic:

Title:

The Adaptive Curve Evolution Model for Interest Rates

Abstract:

In this talk, the speaker presents the key results from his recent book of the same title. The ACE model—in its original form developed by Gregory Pelts and now carefully rephrased, refined, and made more accessible by Matthias Heymann—is the first to combine all of the most desirable analytical properties in one interest rate model: It is low-dimensional (with any dimension n ∈ ℕ\{2}), complete (i.e., it models all tenors), arbitrage-free, highly flexible (it provides 2n+1 discrete parameters, plus the functional noise parameter σ(x,t)), and time homogeneous if desired, and it imposes a lower bound on rates; moreover, it has the rare feat of being unspanned (i.e., its bond price function does not depend on σ), which simplifies calibration. While its original derivation relied on an arsenal of compelling tools borrowed from theoretical physics (in particular, Einstein’s Special Theory of Relativity), the model’s form presented in this talk will only require basic mathematical skills.

Bio:

Matthias Heymann has a Ph.D. in mathematics (2002–07, Courant Institute of Mathematical Sciences, NYU) and did a postdoc in the Duke University Mathematics Department (2007–10). Specializing in probability theory, during his academic career he made contributions related to Wentzell–Freidlin theory, i.e., the study of maximum likelihood transition curves in stochastic dynamical systems with small noise. One of his most notable publications is his monograph “Minimum Action Curves in Degenerate Finsler Metrics — Existence and Properties,” published in Springer’s “Lecture Notes in Mathematics” series.

In 2010 he started working as a quantitative analyst at Goldman Sachs. During this time he began his work on the ACE model, which eventually turned into his second book, whose results are presented in this talk.

We look forward to having you join us for the talk and refreshments. Please mark your calendars.

Seminar

October 2, 2019: FinTech Seminar Series

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Join us on October 2nd for a discussion about Dynamic Replication and Hedging: A Reinforcement Learning Approach presented by Petter Kolm.

About this Event

In this talk we address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model.

Based on reinforcement learning (RL), a well-established machine learning technique we propose a model that is flexible, accurate and very promising for real-world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All that it needs is a good simulator, in which transaction costs and options prices are simulated accurately.

This is joint work with Gordon Ritter.

Published Paper:
https://jfds.iijournals.com/content/1/1/159

View Peter Kolm’s Profile.

 

Location

Manhattan Institute of Management
2 Washington Street
17th Floor
New York, NY 10004