Quantitative Methods and Machine Learning: Jan 2021

NLP, Machine Learning, and Quant – Top Recent Papers

Nowadays, many discussions of quantitative methods revolve around factor investing, but quant can also take on a variety of other forms. Machine learning (ML) techniques are increasingly being used to enhance financial models, finding new statistically significant variables that would likely go undetected by the human eye.

State Street looks at how we can validate the output from ML-enhanced models as both trustworthy and applicable. McKinsey & Company explains the basic premises of quantum computing and the beneficial characteristics of qubits for investment managers of the future. Meanwhile, investment managers of the present who are at all curious about the formulas that constitute index mathematics should turn their gaze to the second paper on the below list from S&P Dow Jones Indices.

RECENT QUANTITATIVE PAPERS

More Bullish Than Ever on Quant (Robeco, Nov 2020)

Despite the recent underperformance of quantitative strategies, David Blitz of Robeco believes that the quant winter could soon be coming to an end.

Index Mathematics – Methodology (S&P Dow Jones Indices, 2020)

Ever wondered about the mathematics that underlies equity index calculations? In this 71-page publication, S&P Dow Jones Indices explains the methodology behind every single one of their indices.

Next-Generation Commodity Benchmarks (PGIM IAS, Nov. 2020)

This paper uses a type of real asset sensitivity analysis to demonstrate how chief investment officers can build custom commodity benchmarks.

Liquidity Stress Testing in Asset Management (Amundi, 2021)

For compliance reasons, this paper is not accessible in the United States

Amundi seeks to propose a framework for liquidity stress testing. This article is the first of a three part series on liquidity risk in the asset management industry. It contains an in-depth analysis of redemption shocks.

Do Factors Carry Information About the Economic Cycle? (FTSE Russell, 2020)

FTSE Russell examines the behaviour of four factors (Size, Value, Momentum, and Quality) across the past 60 years of economic cycles.

Economic Activity: Insights from machine learning (US Federal Reserve, 2020)

The author uses machine learning to build an index of financial conditions, composed of variables that are able to predict future unemployment data.

Five Lessons on Machine Learning-Based Investment Strategies (FactSet, 2021)

FactSet uses machine learning to enhance a model that assesses the potential for outperformance within the China A-share market. Five lessons are then provided as a guide for machine learning implementation.

How Quantum Computing Could Change Financial Services (McKinsey & Company, 2020)

Qubits could hold the key to an immense amount of processing capability. This has the potential to benefit complex models within the investment industry and many others that rely on large data sets.

Can We Trust Machines to Pick Stocks? (State Street, 2020)

Are machines reliable stock pickers? State Street looks at the drivers behind the predictions of machine learning models in order to reveal output that is interpretable, investable, and interesting.

Value Investing: Improving the Piotroski F-score measure (Alpha Architect, 2020)

Beginning with the work of J. Piotroski on the value factor, the author uncovers several statistically significant variables to build upon earlier work and formulate a new and improved model.

The Alternative Data Imperative, Part 1 (AllAboutAlpha, 2020)

This two-part series discusses the definition of alternative data, the forms that it can take on, and many of its applications for investment professionals.

Are Private Equity Investors Fooled by IRR? (2020)

Private equity firms have the ability to manipulate the very distributions and capital calls that impact their internal rates of return, and many choose to do so. How can sophisticated investors better account for this phenomenon?

Do Sectors Matter in Fixed Income? Less Than You May Think… (Qontigo, 2020)

A factor-based model from Axioma is used to examine the importance of fixed income sector allocations in credit portfolios, with surprising results.

Risk & Reward Q4 2020: The pace of innovation (Invesco)

For compliance reasons, this paper is only accessible in certain geographies

Invesco’s Q4 edition of their Risk & Reward magazine focuses upon the acceleration of innovation, via new inventions and digitalisation. The second article discusses the application of Natural Language Processing techniques to uncover investment trends.

SAVVY INVESTOR AWARDS

Best Quant Paper 2020 (Savvy Investor Awards)

The winner of the 2020 Savvy Investor Awards "Best Quant" paper was CFA Institute Research Foundation. For more information about their paper, as well as the other Highly Commended papers recognised in the Awards, click on the link above.

ABOUT THE AUTHOR

Andrew Perrins is a former Actuary and Asset Allocator. After qualifying as an Actuary, he worked for 15 years in investment management, serving as Director of Asset Allocation for Abbey Life and for Chase Manhattan, before setting out on a more entrepreneurial path.

To contact him, email [email protected]