Corporate accountability, reporting transparency, and standard regulatory disclosures appear to be key to determining whether the commitment a company states it is making is indeed aligned with its actions. Well, we'll provide a broad overview of how firms may mislead investors about their ESG related practices. We'll first explore big data further and how the trove of information certain data collectors are able to harness may, on their own, not be sufficient when weeding out risks such as green-washing. How to employ big data has become an increasingly integral component in making environmental, social, and governance related investment decisions. Indeed, as investors typically find conducting fundamental analysis on ESG related issues daunting, many companies and organizations have been working to establish more advanced technological tools with scoring and ranking systems to aid in the effort. In the webinars that follow, you'll learn how less traditional financial sources, namely investments signals from social media, news, and blogs, increasingly offer meaningful insights on market momentum shifts as they occur. The first to these presentations, Marina Goche, Chief Executive Officer at alternative data providers, Sentifi, evaluates what constitute good data for investment decision-making, how alternative data stacks up, and key considerations in selecting alternative data sources to make informed investment decisions. Among other features in this webinar, Goche 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. Next, 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 ESG factors in terms of materiality. They also offer, among other commentary, how ESG investing and big data may evolve over the next 5-10 years. But first, stay with us now for Sentifi's presentation of making informed investment decisions with alternative data. Coming up next. In terms of my background, I have over 24 years experience in the financial information sector as well as with investment banks. The likes of Markit IHS, S&P Global, Barclays, and State Street Bank. As you mentioned earlier, Sentifi is an award winning alternative data provider and I'm excited to be discussing what that means and also how investors can leverage alternative data to make informed investment decisions. Effectively, when you consider financial information and more importantly, what defines quality with respect to financial information. What are those characteristics? The reason why we're focusing on that is because as we evaluate alternative data and how it stacks up, this criteria becomes particularly important. In order to capture new investment opportunities and manage investment risk, there's quite a simple framework here for an investor who is looking to assess quality of the information that is input into that process. The first characteristic really is that the dataset needs to be reliable. Which goes without saying, but what does it mean to be reliable? Reliable means that the dataset needs to be accurately processed first and foremost of all and the source of the raw data needs to be verified. As an investor, you clearly want to know the source of the information. The second characteristic of quality financial information is that the dataset needs to be granular and consistently available on every company to assess and compare peers in the same way. For example, if the dataset is only available for stocks listed in certain markets, investors are having to look for comparable data on other markets in another way, which is not ideal. The third characteristic of what defines quality information is that the dataset needs to be timely. If there is a time lag in the production and availability of the dataset, it decays clearly the value of that dataset to the investor in the investment decision-making process. Of course, finally, the dataset needs to be actionable. The investor really should be able to make a buy, hold or sell decision based on the insights that dataset offers. Traditionally, financial datasets like economic or fundamental data, may meet the reliability, granularity, and actionability criteria, but fall short on the timeliness factor which is essential with the market volatility today and will be increasingly important to time market entry and exit as COVID recovery rates become clearer in different markets. Alternative data can really close that gap with the timeliness factor with traditional financial data sets. It can also close the granularity and reliability gap by really providing access to market momentum shifts as it's happening. Which as we saw really with gain stock and the main stock rallies this year is an important element for investors moving forward.
