Event Details:
Please join us for a webinar of the AI & Big Data in Finance Research Forum (ABFR):
The webinar is on September 26, 9-10am Pacific Time (12-1pm ET)
Presenter: Robert Richmond (New York University)
Discussant: Yinan Su (John Hopkins University)
Zoom webinar link:https://stanford.zoom.us/j/99915167642?pwd=WLh3pfXSMkhV82VGRjGGai1qKydMBg.1
Meeting ID: 999 1516 7642
Passcode: 768565
For more information, please visit our website:https://www.abfr-forum.org
Asset Embeddings
Authors: Xavier Gabaix (Harvard), Ralph S. J. Koijen (Chicago), Robert Richmond (NYU) and Motohiro Yogo (Princeton)
Abstract: Firm characteristics are ubiquitously used in economics. These characteristics are often based on readily-available information such as accounting data, but those reflect only a part of investors' information set. We show that useful information about firm characteristics is embedded in investors’ holdings data and, via market clearing, in prices, returns, and trading data. Based on insights from the recent artificial intelligence (AI) and machine learning (ML) literature, in which unstructured data (e.g., words or speech) are represented as continuous vectors in a potentially high-dimensional space, we propose to learn asset embeddings from investors’ holdings data. Indeed, just as documents arrange words that can be used to uncover word structures via embeddings, investors organize assets in portfolios that can be used to uncover firm characteristics that investors deem important via asset embeddings. This broad theme provides a natural bridge to connect recent advances in the fields of AI and ML to finance and economics. Specifically, we show how language models, including transformer models that feature prominently in large language models such as BERT and GPT, can handle numerical information, and in particular holdings data to estimate asset embeddings. We provide initial evidence on the value added of asset embeddings through a series of applications in the context of firm valuations, return comovement, and uncovering asset substitution patterns. As a by-product, the models generate investor embeddings, which can be used to measure investor similarity. We propose a programmatic list of potential applications of asset and investor em- beddings to finance and economics more generally.
Bio of speaker: Robert J. Richmond is an Associate Professor of Finance at NYU Stern and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). My research interests are in asset pricing and international finance. His research has been published in outlets such as the Journal of Finance, the Review of Financial Studies and Journal of Financial Economics. He earned his Ph.D. in Finance from the Anderson School of Management at the University of California, Los Angeles in 2016 and his B.S. in Applied Mathematics from University of Colorado at Boulder in 2011.
Bio of discussant: Yinan Su is an Assistant Professor of Finance at the Carey Business School of the Johns Hopkins University. His research interests include asset pricing, financial econometrics, banking and economic networks. His research has been published in outlets such as the Review of Financial Studies and Journal of Financial Economics. Before joining Carey, Yinan Su earned his PhD degree from the University of Chicago's Joint Program in Financial Economics and a bachelor's degree in economics and finance from Tsinghua University.