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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/esg-data-and-accountability
课程评论：没有评论
课程名称：ESG数据与问责制 课程概述：本课程旨在为具有传统金融产品基础知识的学生介绍数据驱动的资源，以补充他们的基本分析。课程还将强调某些欺骗性营销实践，这些做法可能会美化企业在应对ESG（环境、社会和治理）相关问题方面的实际成就。此外，越来越多的公司意识到千禧一代的价值观，而根据一些行业调查，这一代人似乎占据了那些将ESG作为投资计划中心目标的投资者的绝大多数。 课程内容将深入探讨这些主题，通过视频、网络研讨会和阅读模块等多种形式，您将学习如何解释大数据和人工智能如何用于可操作的问责制，描述数据分析中的固有挑战，并区分不同形式的欺骗性商业行为，包括绿色洗涤、社会洗涤和粉色洗涤。您还将了解到千禧一代及更年轻的投资者的兴趣如何推动对ESG投资的关注增加。 课程大纲： 1. 使用先进技术进行进一步分析：本模块探讨企业如何利用先进的技术工具和评分系统（如人工智能、机器学习和自然语言处理等）来协助ESG相关问题的基本分析。 2. 探索投资情绪信号：来自Sentifi的行业洞见（1-3部分）：该模块通过网络研讨会介绍如何通过社交媒体、新闻和博客等非传统金融来源提供投资决策的有意义见解。 3. 探索投资情绪信号：来自Sentifi的行业洞见（4-6部分）：讨论投资情绪信号如何影响资产估值及其在投资组合再平衡中的应用。 4. 探索投资情绪信号：来自Sentifi的行业洞见（7-8部分）：提供有关投资情绪信号的具体细节，以及如何利用机器学习模型评估信息源的可信度。 5. 大数据与问责制：行业洞见 - 小组讨论（1-3部分）：小组讨论如何在技术与人工分析之间取得平衡，以及如何评估ESG各个因素的重要性。 6. 大数据与问责制：行业洞见 - 小组讨论（4-6部分）：探讨ESG评估中的主观性挑战及其如何与投资者的个人价值观相连接。 7. 大数据与问责制：行业洞见 - 小组讨论（7-8部分）：展望未来5至10年ESG投资的发展，并讨论如何更好地利用ESG数据进行投资决策。 8. 阅读强化模块：深入探讨数据收集、ESG评分及人工智能在ESG投资实践中的应用，及其有关绿色洗涤行为的具体实例。 完成本课程后，您将能够有效地讨论ESG投资相关市场的现状及其面临的挑战，制定符合您价值观的投资决策。
Name:Using Advanced Technology for Further Analysis
Description: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.
Name:Exploring Investment Sentiment Signals: Industry Insights from Sentifi (Parts 1-3)
Description: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.
Name:Exploring Investment Sentiment Signals: Industry Insights from Sentifi (Parts 4-6)
Description: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.
Name:Exploring Investment Sentiment Signals: Industry Insights from Sentifi (Parts 7-8)
Description: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.
Name:Big Data & Accountability: Industry Insights - Panel Discussion (Parts 1-3)
Description: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.
Name:Big Data & Accountability: Industry Insights - Panel Discussion (Parts 4-6)
Description: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.
Name:Big Data & Accountability: Industry Insights - Panel Discussion (Parts 7-8)
Description: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.
Name:Reading Reinforcements
Description: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.
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.
