"An Adaptive Residual-Based Test for Factor Structure", with Yayi Yan, Journal of Business & Economic Statistics (2025), 1-26. [Journal Link] [SSRN Link]
"Impact of Climate Change on Agriculture in Australia: An Interactive Fixed Effects Model Approach", with Natalia Bailey, Zvi Hochman, Mervyn Silvapulle, and Param Silvapulle, Applied Economics (2024), 1-14. [Journal Link]
The Artificial Intelligence Premium [SSRN Link]
with Runfeng Yang, Massimiliano Caporin, and Juan‐Angel Jimenez‐Martin, to be updated
Summary: We study if the AI adoption is priced in the cross-section of US stocks. We find that firms with a higher AI adoption level exhibit higher stock returns. A one-standard-deviation increase in the level of AI adoption indicates a 18 basis-point increase in the expected monthly stock returns. We study several economic channels for the findings. The AI premium is related to the digital benefit and the technological adoption risk. It is the slow realization of early AI investments, and is influenced by shifting investor perceptions of AI technology. The AI premium is also associated with the market sentiment towards AI, and varies across sectors.
Presented at: Universidad Complutense de Madrid; University of Pavia; IFABS 2024 Shanghai Conference, Shanghai; 23rd International Conference on Credit Risk Evaluation, Palazzo Franchetti, Venice; the Generative AI in Finance Conference, Montréal.
Sector Structure in Digital Asset Returns [SSRN Link]
with Runfeng Yang and Massimiliano Caporin, submitted
Summary: We identify a sector structure within the digital asset market, where different types of digital assets (the "digital asset sectors") exhibit distinct risks and returns. We investigate the observed sector variation through two channels: the systematic risk channel and the idiosyncratic risk channel. Our findings indicate that sector variations within the systematic channel are insignificant. In contrast, sector variations within the idiosyncratic channel are substantial, driven by sector-specific events, sentiments, and inter-sector spillovers.
Presented at: University of Verona; 2nd Financial Econometrics Meets Machine Learning Conference, Università della Svizzera Italiana; 2024 Australasian Meeting of the Econometric Society, Monash University.
A Multilevel Latent Factor Model for Longitudinal Network and Relational Data
with Bin Peng, draft available upon request, submitted
Summary: This paper develops a methodology for estimating a multilevel latent factor model for longitudinal network and relational data, where the cross-section of the time series is comprised of entities acting as senders and receivers. The model consists of global factors and separate factors for senders and receivers in the network. This class of models are useful in several areas such as communication, economics, and health sciences. The current methodological literature is not sufficiently advanced for this family of models. We show that our proposed method consistently estimates the number of factors and the factor spaces. The asymptotic results are established for the case when the number of senders, receivers, and time periods are large. We illustrate the method by applying it to model volumes of bilateral trade among 23 countries.