13 citations · 19 across the 8 of their papers we have counts for
10 papers
Do LLMs Understand Limit Order Book Dynamics?
Junxiao Chen, Paul Glasserman
A large language model (LLM) trained on synthetic limit order book (LOB) data achieves near perfect scores in generating valid sequences of LOB events. However, the LLM's implicit…
Differential ML with a Difference
Paul Glasserman, Siddharth Hemant Karmarkar
Differential ML (Huge and Savine 2020) is a technique for training neural networks to provide fast approximations to complex simulation-based models for derivatives pricing and ris…
Do Large Language Models (LLMs) Understand Chronology?
Pattaraphon Kenny Wongchamcharoen, Paul Glasserman
Large language models (LLMs) are increasingly used in finance and economics, where prompt-based attempts against look-ahead bias implicitly assume that models understand chronology…
Does Overnight News Explain Overnight Returns?
Paul Glasserman, Kriste Krstovski, Paul Laliberte +1
Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large pa…
Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis
Paul Glasserman, Caden Lin
Large language models (LLMs), including ChatGPT, can extract profitable trading signals from the sentiment in news text. However, backtesting such strategies poses a challenge beca…
New News is Bad News
Paul Glasserman, Harry Mamaysky, Jimmy Qin
An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribu…
Should Bank Stress Tests Be Fair?
Paul Glasserman, Mike Li
Regulatory stress tests have become one of the main tools for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-…
Linear Classifiers Under Infinite Imbalance
Paul Glasserman, Mike Li
We study the behavior of linear discriminant functions for binary classification in the infinite-imbalance limit, where the sample size of one class grows without bound while the s…
Choosing News Topics to Explain Stock Market Returns
Paul Glasserman, Kriste Krstovski, Paul Laliberte +1
We analyze methods for selecting topics in news articles to explain stock returns. We find, through empirical and theoretical results, that supervised Latent Dirichlet Allocation (…
A Conversation with Chris Heyde
Paul Glasserman, Steven Kou
Born in Sydney, Australia, on April 20, 1939, Chris Heyde shifted his interest from sport to mathematics thanks to inspiration from a schoolteacher. After earning an M.Sc. degree f…