ESG Data & Accountability

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Go to class

Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

20 hours 24 minutes
Sessions

On-Demand
Level

Beginner
Subtitles

English, Kazakh, German, Hindi, Russian, Swedish, Korean, Portuguese, Greek, Italian, French, Chinese, Spanish, Arabic, Thai, Ukrainian, Japanese, Indonesian, Polish, Dutch, Turkish

Found in

Part of

ESG Investing: Financial Decisions in Flux

Overview

In this course, we’ll introduce students with basic knowledge of traditional financial products to data-driven resources they can use to complement their fundamental analysis. We’ll also highlight certain deceptive marketing practices that can paint a rosier picture of addressing ESG-related concerns than may actually be the case. Moreover, many corporations appear to be growing increasingly aware of the values of the millennial generation, who, according to some industry surveys, appear to account for the vast majority of those who cite ESG as a central goal in their investment plans. We’ll dive more deeply into these topics, and through a series of video, webinar, and reading modules, among other objectives, you’ll learn to explain how Big Data and artificial intelligence may be used for actionable accountability, and describe inherent challenges in data analysis, as well as differentiate between different forms of deceptive business practices, including green washing, social washing, and pink washing, You’ll also be able to provide insights about how millennial, and younger, investors’ interests may be driving increased attention on ESG investing.

Syllabus

Using Advanced Technology for Further Analysis

As conducting fundamental analysis on ESG-related issues can be quite a daunting task – especially given the massive amounts of variables that may be considered relevant – many companies and organizations have been working to establish more advanced, technological tools and scoring systems to aid in the effort. For example, certain companies, including Refinitiv, Morningstar’s Sustainalytics, FactSet-owned Truvalue Labs, and MSCI, among others, may employ artificial intelligence, with machine learning, natural language processing (NLP) and sentiment analysis algorithms to help arrive at data that may assist in ESG investing analysis. In this module, we will take a broad look at the landscape of how Big Data and certain impact tools can help guide you through making investments in the ESG space.

Exploring Investment Sentiment Signals: Industry Insights from Sentifi (Parts 1-3)

In the following webinar, “Making Informed Investment Decisions with Alternative Data “, you’ll learn how less traditional financial sources – namely investment signals from social media, news, and blogs increasingly offer meaningful insights on market momentum shifts as they occur. Among other features in this presentation, Marina Goche, chief executive officer at alternative data provider Sentifi, evaluates what constitutes “good data” for investment decision-making, how alternative data stacks up, and key considerations in selecting alternative data sources to make informed investment decisions. Goche also walks through certain characteristics of alternative data quality such as reliability, granularity, timeliness, and actionability, and how these may be used to inform investors’ buy, sell, and hold decisions.

Exploring Investment Sentiment Signals: Industry Insights from Sentifi (Parts 4-6)

The “Making Informed Investment Decisions with Alternative Data” webinar continues with Marina Goche, CEO of Sentifi, illustrating how alternative data may close the timeliness gap with traditional data sets, how investment sentiment signals from ESG events may impact asset valuations, such as a company’s stock price, and explores how investors may rebalance a portfolio using investment sentiment signals based on ESG events.

Exploring Investment Sentiment Signals: Industry Insights from Sentifi (Parts 7-8)

The “Making Informed Investment Decisions with Alternative Data” webinar continues with Marina Goche, CEO of Sentifi, fielding participants’ questions about investment sentiment signals, such as corruption, provides details about how machine learning models can help determine source credibility, and addresses how alternative data may be considered “actionable” when making investment decisions and rebalancing a portfolio. Goche also highlights certain technology features designed to handle Sentifi’s massive volume of alternative data sets.

Big Data & Accountability: Industry Insights - Panel Discussion (Parts 1-3)

In the webinar, “ESG Investing: How Big Data Helps Drive Financial Decisions”, panelists from Refinitiv, FactSet-owned Truvalue Labs, Quantum Research Group, and IBKR provide their insights into a wide range of topics, including finding the right balance between technology and human analysis, as well as how much weight to assign to each of the E-S-G factors in terms of materiality. They also offer, among other commentary, how ESG investing and Big Data may evolve over the next 5 to 10 years.

Big Data & Accountability: Industry Insights - Panel Discussion (Parts 4-6)

The “ESG Investing: How Big Data Helps Drive Financial Decisions” webinar continues with panelists addressing the challenge of subjectivity when assessing financial materiality in ESG, how the mentality of the financial market has shifted towards ESG, sustainability and impact, and how ESG factors can connect with investors’ personal values when making investment decisions.

Big Data & Accountability: Industry Insights - Panel Discussion (Parts 7-8)

The “ESG Investing: How Big Data Helps Drive Financial Decisions” webinar continues with panelists providing their outlook on how ESG investing may evolve over the next 5-10 years, as well as addresses participants’ questions about how Big Data interprets ESG factors, and how ESG data can be used to make more informed investment decisions. Following the webinar, we will also walk-through different forms of deceptive business practices, including green washing, social washing, blue washing, and pink washing, and how companies have historically sought to sidestep controversies, and their associated, potential adverse financial impacts. You’ll learn what forces can help cajole these companies from being held accountable, as well as how regulatory reform may play a key role in eliminating these practices. After completing the videos and webinars in this module, you should be able to discuss, among other topics, how, despite the proliferation of ESG investing-related marketing, products, research, data-driven tools, and inflows into funds, the discipline itself remains subject to a host of unresolved issues, and how conducting due diligence when integrating ESG factors into your financial and credit analyses may ensure whether your investments will be truly aligned with your values.

Reading Reinforcements

This Reading Reinforcements module explores more in-depth details about data collection, ESG scores, and how artificial intelligence can aid ESG investing practices, as well as specific examples of green washing activity. After completing the lessons that follow, you should be able to draw conclusions about how Big Data and artificial intelligence can aid in ESG investing analysis, as well as provide insights into historical, and more recent, forms of deceptive business practices.