Structured Event Representation and Stock Return Predictability

We find that event features extracted by large language models (LLMs) are effective for text-based stock return prediction. Using a pre-trained LLM to extract event features from news articles, we propose a novel deep learning model based on structured event representation (SER) and attention mechanisms to predict stock returns in the cross-section. Our SER-based model provides superior performance compared with other existing text-driven models to forecast stock returns out of sample and offers highly interpretable feature structures to examine the mechanisms underlying the stock return predictability. We further provide various implications based on SER and highlight the crucial benefit of structured model inputs in stock return predictability.

AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform

Artificial intelligence (AI) refers to the ability of machines or software to mimic or even surpass human intelligence in a given cognitive task. While humans learn by both induction and deduction, the success of current AI is rooted in induction, relying on its ability to detect statistical regularities in task input -- an ability learnt from a vast amount of training data using enormous computation resources. We examine the performance of such a statistical AI in a human task through the lens of four factors, including task learnability, statistical resource, computation resource, and learning techniques, and then propose a three-phase visual framework to understand the evolving relation between AI and jobs. Based on this conceptual framework, we develop a simple economic model of competition to show the existence of an inflection point for each occupation. Before AI performance crosses the inflection point, human workers always benefit from an improvement in AI performance, but after the inflection point, human workers become worse off whenever such an improvement occurs. To offer empirical evidence, we first argue that AI performance has passed the inflection point for the occupation of translation but not for the occupation of web development. We then study how the launch of ChatGPT, which led to significant improvement of AI performance on many tasks, has affected workers in these two occupations on a large online labor platform. Consistent with the inflection point conjecture, we find that translators are negatively affected by the shock both in terms of the number of accepted jobs and the earnings from those jobs, while web developers are positively affected by the very same shock. Given the potentially large disruption of AI on employment, more studies on more occupations using data from different platforms are urgently needed.