Research Interests
Corporate Finance, Empirical Asset Pricing, Investment, FinTech
Publications
“Momentum in machine learning: Evidence from the Taiwan stock market” (with Dien Giau Bui, Chih-Yung Lin, and Tse-Chun Lin), Pacific-Basin Finance Journal, Forthcoming (SSCI) | SSRN | PDF |
The PBFJ Special Issue on the 30th anniversary of the landmark seminal paper “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” by Prof. Sheridan Titman and Prof. Narasimhan Jegadeesh.
Working Papers
“Is love blind? AI-powered trading with emotional dividends”
(with Valeria Fedyk and Daniel Rabetti) | SSRN
We leverage the non-fungible tokens (NFTs) setting to assess the valuation of emotional dividends (LOVE), a long-standing empirical challenge in private-value markets such as art, antiques, and collectibles. Having created and validated our proxy, we use deep learning algorithms and discover that contemporaneous price fluctuations, collection features, and ownership wealth significantly contribute to the formation of LOVE. Understanding the drivers of LOVE, we employ AI-powered algorithms to estimate the prices of NFTs. While AI models accurately predict NFT prices, the performance is decreasing in LOVE. Finally, we demonstrate that LOVE-driven trading leads to significant financial losses over the long term, suggesting that some traders trade off wealth for emotional utility. Our study provides novel economic insights into the factors behind emotional dividends and their role in the pricing of private-value assets. It also highlights the challenges AI-powered trading faces in markets with too much LOVE.
“Alternative investments in the Fintech era: The risk and return of non-fungible token (NFT)”
(with Tse-Chun Lin) | SSRN
This paper is one of the earliest studies in the literature. As a pioneering work, it has been downloaded over 9,000 times on SSRN.
Semifinalist for Best Paper Award, FMA Annual Meetings, 2022
Best Paper Award, International Conference of the Taiwan Finance Association (TFA), 2022
Our study highlights the NFT rarity as a key determinant of price premium in the cross-section. Moreover, well-connected investors, who establish their central positions in the NFT network through early adoption and active trading, enjoy pricing advantages. As an investment class, NFTs exhibit a high-return and high-risk profile when compared to traditional assets, especially in a low-interest-rate environment.
“Collective reputations, the trust premium, and corporate misconduct”
(with Konan Chan and Tse-Chun Lin) | SSRN
We investigate how collective country reputations influence foreign stock prices after outbreaks of corporate misconduct. We find that investors in U.S. stock markets penalize not only misconduct firms but also other foreign firms from the same country, especially among those from more trustworthy countries. The effect is more pronounced for foreign stocks with a poorer information environment.
“Employees as corporate stakeholders: The effect of organized labor on reporting discretion”
(with Ching-Hung Chang and Yung-Ling Chi)
We find that firms with organized labor provide earnings with better quality. The influence of organized labor is significantly stronger in firms with greater capacity to wield influence, enhanced incentives to monitor, and in contexts where alternative information sources are limited. The impact of organized labor on increasing labor-related costs is also significantly reduced in firms with better earnings quality. Overall, our research indicates that managers face a tradeoff between shielding shareholders from union-driven rent-seeking and building a trusting relationship with employees by accommodating employees’ demand for useful information.