Weathering the Next ‘Great’ Storm in the Market

By Bill McCoy and Henri Waelbroeck

September 1, 2018

This is not an article about the joys and wonders of machine learning or big data. Instead, this article is about a pernicious, unsolved problem in investment finance we continue to ignore, for which machine learning and big data may represent one probable solution.

Every time there is a correction in the stock market, or a recession in the economy, there are doomsayers who proclaim that the inevitable next step is a “great” recession, or perhaps even a depression. While corrections and recessions may lead to financial crises, a more certain intermediate step is the collective fear that all market participants are (or at least may be) tainted, a fear that negatively impacts trading volumes and liquidity at a time when all market participants seek to rebalance their portfolios.

This vicious cycle could, of course, be broken if no one had to trade in that moment; however, regulations designed to stave off further pain can sometimes force trading under such unfavorable conditions. An unfortunate side effect of well-intentioned regulations, this kind of trading in crisis is considered (il)liquidity risk as portfolio managers seek to bring their holdings into a new definition of compliance.

(janews/Shutterstock)

Unfortunately, the current state of liquidity risk modeling is not up to the task of anticipating such events and informing decision making. The definition of the problem varies from participant to participant. The assumptions of linearity and normal probability distributions just aren’t accurate. The data itself is often hidden or unavailable.

Fortunately, this is not a call to arms to begin stockpiling supplies for some impending doomsday. Work is underway to modify analytical models to better model joint tail dependencies, including academic proposals such as nested factor models, and enhancements to commercial risk models, including some that use non-pseudo-elliptical copula models to simulate the effect of market turbulence on fat tail risk.

Another part of the solution is the development of powerful machine learning (ML) methods that combine many sources of information into, for example, an estimate of the probability of future events that the market might not be accounting for. Even if improved analytical models are able to predict the outcome of certain scenarios, such a capability would be useless if we didn’t know what kind of scenarios we need to worry about, and what their relative probabilities are.

Big data has permeated the financial industry in many areas: generating alpha models, optimizing trade execution, and estimating news relevancy, just to name just a few. Thus far, however, it has not had much impact on risk models. A better understanding of risk as a predictive methodology is required before the next stampede; we will argue here that recent advances in machine learning and big data are just what is needed to accomplish this end.

Big data techniques enable systematic screening for relevant information to predict the relative likelihood of various scenarios. One problem that complicates the application of machine learning in finance is that the underlying system changes over time. This problem is called “concept drift” in the machine learning world, and it affects conventional risk models as well as ML models.

Fortunately, recent developments in ML applications, such as alpha profiling in trade execution, have led to techniques that help identify features that are more resilient to concept drift, leading to  improved generalization power. In addition, coupling scenario probability estimation to scenario-specific coefficient estimates can yield a class of models that is effectively able to automatically “switch” between different behaviors as potentially catastrophic events unfold. Algorithm switching is now a well-established technique in institutional trade execution, for example. Absent a “theory of everything,” perhaps what is most needed is a diverse ecosystem of models together with an understanding of each model’s validation domain.

But why should a portfolio manager focus attention on risk models, instead of simply focusing on generating alpha? If burgeoning financial crises can require many asset managers to adjust their portfolios in the same way at the same time, a model that is able to anticipate such a change can help prepare a portfolio for such a wave, and thus better navigate its effects. This has value as a defensive tactic, to avoid painful liquidations under stress, but also as a source of alpha: a manager able to anticipate a liquidity crisis can both avoid the liquidations and position her portfolios to take advantage of the mispricings that will develop during and following the crisis.

Machine learning can reveal non-stationarities in risk model coefficients, and the time derivative of risk is alpha. Thus, the first adopters of machine learning-enhanced risk models may be portfolio managers rather than risk managers. Of course, this is all speculation on the part of the authors. We will only fully be able to verify our assertions when we can ask the survivors of the next financial crisis how they navigated troubled waters, and how they prepared in the doldrums.

About the authors: Bill McCoy is a Senior Vice President in the Analytics business unit at FactSet, a provider of financial solutions. In this role, he actively works in research, client support, and sales to help the firm enhance its position as a leading provider for comprehensive valuation and risk analytics for fixed income securities and the derivatives used to hedge them. . Prior to FactSet, Bill worked for other fixed income software vendors as well as in fixed income portfolio management. 

Henri Waelbroeck, Ph.D., is Vice President, Director of Research for Portfolio Management & Trading solutions at FactSet. Previously, he served as the Global Head of Research for Portware, a FactSet Company. Waelbroeck leads the firm’s Alpha Pro research, applying machine learning and artificial intelligence to optimize execution management. Prior to joining Portware, he was Director of Research for Aritas Group, Inc., co-founded Adaptive Technologies Inc., and served as Research Professor at the Institute for Nuclear Sciences at UNAM, Mexico.

 

This article originally appeared on HPCwire sister publication Datanami.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Top Ten Ways AI Affects HPC in 2019

March 26, 2019

AI workloads are becoming ubiquitous, including running on the world’s fastest computers — thereby changing what we call HPC forever. As every organization plans for the future, AI workloads are on our minds — how Read more…

By James Reinders

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, provided a brief but insightful portrait of Nvidia’s rese Read more…

By John Russell

ORNL Helps Identify Challenges of Extremely Heterogeneous Architectures

March 21, 2019

Exponential growth in classical computing over the last two decades has produced hardware and software that support lightning-fast processing speeds, but advancements are topping out as computing architectures reach thei Read more…

By Laurie Varma

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Insurance: Where’s the Risk?

Insurers are facing extreme competitive challenges in their core businesses. Property and Casualty (P&C) and Life and Health (L&H) firms alike are highly impacted by the ongoing globalization, increasing regulation, and digital transformation of their client bases. Read more…

Interview with 2019 Person to Watch Jim Keller

March 21, 2019

On the heels of Intel's reaffirmation that it will deliver the first U.S. exascale computer in 2021, which will feature the company's new Intel Xe architecture, we bring you our interview with our 2019 Person to Watch Jim Keller, head of the Silicon Engineering Group at Intel. Read more…

By HPCwire Editorial Team

Top Ten Ways AI Affects HPC in 2019

March 26, 2019

AI workloads are becoming ubiquitous, including running on the world’s fastest computers — thereby changing what we call HPC forever. As every organization Read more…

By James Reinders

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, Read more…

By John Russell

At GTC: Nvidia Expands Scope of Its AI and Datacenter Ecosystem

March 19, 2019

In the high-stakes race to provide the AI life-cycle solution of choice, three of the biggest horses in the field are IBM, Intel and Nvidia. While the latter is only a fraction of the size of its two bigger rivals, and has been in business for only a fraction of the time, Nvidia continues to impress with an expanding array of new GPU-based hardware, software, robotics, partnerships and... Read more…

By Doug Black

Nvidia Debuts Clara AI Toolkit with Pre-Trained Models for Radiology Use

March 19, 2019

AI’s push into healthcare got a boost yesterday with Nvidia’s release of the Clara Deploy AI toolkit which includes 13 pre-trained models for use in radiolo Read more…

By John Russell

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Quick Take: Trump’s 2020 Budget Spares DoE-funded HPC but Slams NSF and NIH

March 12, 2019

U.S. President Donald Trump’s 2020 budget request, released yesterday, proposes deep cuts in many science programs but seems to spare HPC funding by the Depar Read more…

By John Russell

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This