Analyzing Capital Flows of Exchange-Traded Funds
Recorded on April 13, 2018.
- Presentation Slides (PDF, 31 MB)
- BayesiaLab Network File: ETF Flow by Investment Focus Area (XBL, 249 KB)
- Data File for ETF Flow by Investment Focus Area (CSV, 534 KB)
- BayesiaLab Network File: Asset Under Manage Change by Ticker Symbol (XBL, 3 MB)
- Data File for Asset Under Manage Change by Ticker Symbol (CSV, 6 MB)
Given the popularity of our S&P 500 example for Unsupervised Learning, which we have used in countless seminars over the years, we now return to studying financial markets. In this webinar, we focus on Exchange-Traded Funds (ETFs), which have become a very popular form of index-based investing since their inception in the 1990s. ETFs allow investors to gain index-based exposure to "baskets of assets" (i.e., certain markets, geographies, industries, commodities, currencies) without having to buy the underlying assets directly. By definition, ETF shares closely mirror the performance of the underlying assets.
As opposed to other equities, ETF shares can be created and redeemed based on demand or the lack thereof. As a result, each ETF's Assets Under Management (AUM) fluctuates. This AUM change is measured in terms of inflows and outflows. Studying these flows is the subject of this webinar. More specifically, we wish to examine how the flows of over 2,000 ETFs all relate to each other.
As each ETF tracks a specific basket of assets, we can perhaps uncover patterns of flows between them. Perhaps investors are alternating their investment focus between certain sectors or between different parts of the world. This is what we would like to find out. As such, this is a prototypical high-dimensional knowledge discovery task that is ideally suited for BayesiaLab's Unsupervised Learning algorithms.
Given that any investor's capital resources are limited, we explore questions related to substitution and cannibalization between ETFs offered by the same issuer, a topic which we had already discussed in the context if the auto industry in a previous webinar.
|December 10–12, 2019||New York, NY, USA||3-Day Introductory Course|
|February 5–7, 2020||Singapore||3-Day Introductory Course|
|February 10–12, 2020||Sydney, NSW, Australia||3-Day Introductory Course|
|March 3–5, 2020||Dubai, UAE||3-Day Introductory Course|
|March 9–11, 2020||Dubai, UAE||3-Day Advanced Course|
|March 24–26, 2020||Boston, MA, USA||3-Day Introductory Course|
|April 7–9, 2020||Paris, France||3-Day Introductory Course|
|May 6–8, 2020||Seattle, WA, USA||3-Day Introductory Course|
|May 11–13, 2020||Seattle, WA, USA||3-Day Advanced Course|
|June 15–17, 2020||Paris, France||3-Day Advanced Course|
|October 5–7, 2020||Toronto, ON, Canada||3-Day Introductory Course|
|October 13–15, 2020||Toronto, ON, Canada||3-Day Advanced Course|
Seminars, Webinars, and Conferences
|December 12, 2019
2 p.m. – 5 p.m. (EST, UTC-05)
|Free Seminar in New York, NY||Artificial Intelligence for Judicial Reasoning|
|January 21, 2020
2 p.m. – 5 p.m. (EST, UTC-05)
|Free Seminar in Washington, DC||Artificial Intelligence for Judicial Reasoning|
|January 28, 2020, 11 a.m. – 12 p.m. (CST, UTC-06)||Live Webinar||Bayesian Parameter Estimation for Individualized Drug Dosing|
|January 30, 2020
2 p.m. – 5 p.m. (CST, UTC-06)
|Free Seminar in Chicago, IL||Artificial Intelligence for Judicial Reasoning|
|Please check out our archive of recordings of previous events.|