Introduction and Book Overview

1. OVERVIEW

ESG (environmental, social, and corporate governance) finance is a rapidly growing area of investment management - and finance more broadly - that has received a lot of attention in the past several years from the investor community, financial regulatory agencies, and the general public alike. This book introduces ESG finance from a quantitative analyst's perspective.

The authors are not aware of any existing publication that focuses specifically on the quantitative side of ESG finance, while - due to increasing reliance of ESG analyses on alternative data and the need to process it efficiently - there is an undeniable interest and need for such materials.

The ESG acronym refers to key risk factors that reflect the sustainability and societal impact of an investment, and socially responsible investing (SRI) seeks to combine financial returns with the achievement of social and environmental goals and progress. Within the last three years, responsibly managed assets under professional management in the US almost doubled, and a vast majority of investors now identify ESG risk factors as an area of interest.

This book combines the theoretical and quantitative basis underlying risk factor investing and risk management with an in-depth discussion of ESG applications. Both investment management (buy-side) and investment banking (sell-side) applications are discussed. For an investment manager, areas of particular interest are an exposition of risk factor investing, portfolio and index construction, as well as ESG scoring. From a banker's perspective, areas of focus are a newly emerging class of ESG-driven financial products, as well as financial risk management applications, some of which are driven by new financial regulation. These focus topics constitute the core of the book's contents.

Common to both perspectives is the paramount importance of both "traditional" and "alternative" data now available to financial professionals. This book discusses proliferation of newly available data sources and the associated quantitative techniques necessary to process them.

Importantly, a major component of this book is a discussion of climate risk, an area of increasing focus. The book includes an overview of recent advances and the evolving regulatory landscape in the climate risk space. An in-depth discussion of financial impact assessment of various climate risk-driven scenarios (climate risk stress testing) concludes the book.

Both theoretical and practical views on the same topics are presented in the book. To facilitate this, in each chapter, theoretical discussion is supplemented with code snippets and a walkthrough of a Python Jupyter notebook that makes use of publicly available data to demonstrate on a practical example the techniques introduced in the chapter. These Jupyter notebook exercises allow the reader to immediately attempt to apply learned techniques to data.

2. WHY ESG FINANCE?

ESG is not a niche strategy anymore. According to recent Bloomberg analysis,1 ESG assets may reach $53 trillion by 2025, a third of total projected assets under management. This assumes that the growth continues at a 15% pace, half of the average growth rate of the last two years.

Currently, the EMEA (Europe, the Middle East and Africa) region is the global leader in ESG adoption and accounts for approximately half of global ESG assets, while US ESG has experienced the fastest growth in 2020-2021, a trend that is expected to continue, followed by an expansion of the ESG space in Asia, a region currently lagging in ESG adoption.

Further development of the ESG market in EMEA provides hints for what to expect globally: Explosive expansion in new product development is expected to continue, with climate change as the dominant theme, accounting for approximately a quarter of new ESG funds launched last year.

Beyond ESG equity investments, ESG debt markets are also poised for explosive growth. Green, social, and sustainability bonds may already have exceeded $2 trillion in cumulative issuance in 2021. The growth in ESG debt is also partially driven by the post-pandemic recovery across continents.

This is but a single illustration of a persistent trend where ESG investment and ESG finance are rapidly rising in prominence. The percentage of requests for proposals to asset managers that require at least some variant of ESG tracking/screening is growing exponentially across regions (again, led by EMEA at this time).

3. WHY QUANTITATIVE METHODS?

As the amount of overall data available to investors grows rapidly, the proportional share of "unstructured" data also increases. ESG finance has an even stronger reliance on unstructured or "alternative" data, because the traditional structured sources for ESG data are - at the moment - still very inconsistent. ESG data are largely self-reported by companies, and even reporting by companies within the same industry is voluntary and far from standardized. (The book discusses in detail the lack of standardization in ESG reporting and, therefore, in the resulting ESG ratings.) Due to the lack of reporting standards, investors are largely left to rely on alternative data sources. Fintech startups have taken the lead in offering both ESG rating services and the underlying data used in ratings' generation and bespoke ESG analyses.

New data sources require entirely new methods of processing them and a completely novel set of skills for a quantitative professional interested in analyzing the ESG space. Prominent examples include techniques for natural language processing and processing of satellite imagery - both topics discussed in detail in this book.

The book assumes that readers are familiar with basic finance concepts such as Markowitz portfolio theory; it also assumes the audience possesses working knowledge of Python and its package ecosystem, including pandas (or another appropriate scripting language such as R). This is a standard requirement for any modern quantitative professional, as Python (and, to a lesser degree, R) has become a lingua franca of modern data science and quantitative finance.

4. TARGET AUDIENCE AND TIMING OF THIS BOOK

There are two primary categories of readers that would be interested in this book:

• Graduate students of quantitative finance who are interested in this large and rapidly emerging topic but are currently facing a severe lack of appropriate and relevant textbooks, largely because the field of ESG finance is still evolving.
• Finance professionals who have experience applying traditional quantitative and statistical techniques to more traditional areas of finance (e.g., derivative pricing or high-frequency trading of securities) and now face the growing need to analyze ESG markets and financial risks and opportunities in the ESG space but realize that traditional quantitative techniques are insufficient or downright inadequate.

As mentioned earlier, ESG finance has rapidly emerged as a significant area of finance broadly, while even the core definitions within the ESG space continue to evolve. This creates an already significant - and growing - need for new quantitative techniques, which has flourished recently with the growing availability of alternative data, as well as the need for a text available to a wide array of practitioners and students of finance alike that can describe these new techniques in a consistent and accessible form.

5. BOOK OUTLINE

The book follows (and expands upon) a course one of the authors has recently taught to students of the Financial Engineering Master's program at Columbia University as an adjunct professor.

Chapter 1. Introduction to ESG Finance. This chapter introduces readers to the current landscape of ESG finance both from a buy-side and sell-side perspective and describes recent evolution of this area of finance, its recent challenges, and future opportunities.

Such topics as the general definition of ESG finance, ESG funds and indexes, green bonds, basics of climate risk, carbon trading and pricing, and relevant banking regulation and risk management are introduced.

This chapter also includes Jupyter notebook code that demonstrates techniques for retrieving ESG data freely available online, such as web scraping.

Chapter 2. Factor Investing and Smart Beta. This chapter lays a theoretical groundwork for most of the rest of the material. It starts by discussing the basic principles of systematic portfolio construction and performance evaluation and provides an exposition of factor investing and the concepts of risk factor beta and "smart beta." Modern approaches to risk factor indexes' construction are discussed.

In the same chapter, readers also observe the process of a simple beta-tracking index creation through a series of step-by-step code examples. Similarly, the chapter includes a detailed discussion of code used to estimate risk premia series.

Chapter 3. ESG Ratings. Multiple index and analytics providers have recently emerged, offering differing (and sometimes conflicting) takes on ESG risk factors' quantification. This chapter discusses different concepts of rating score construction, along with a methodology for inference of main drivers of ratings.

Hands-on examples from Chapter 3 are extended to adopt specific scoring methodologies common in the ESG...