Yanru HAN

Academic Position

2023 - present                                  Assistant Professor of Finance, Stevens Institute of Technology

Education

2018 - 2023                                  PhD in Finance, The Chinese University of Hong Kong

2014 - 2018                                  B.S. in Economics and Finance, School of Economics and Management, Tsinghua University

Research

“What Do Questions Reveal? Analyst Topic-Specific Skill and Forecast Accuracy” (with Ling Cen and Jarrad Harford) Revise and Resubmit at Review of Financial Studies

Best Paper Award, AsianFA Annual Conference, 2022

Abstract:

Judging skills exclusively based on ex post outcomes leads to biased evaluation and talent misallocation. We construct an ex ante and generalizable topic-specific skill measure based on the frequency of topic-specific questions that analysts raised in past earnings conference calls. In a supply chain information setting, we show that analysts with supply-chain-specific skill experience a greater improvement in forecast accuracy relative to their peers when the firms experience firm-specific or market-wide supply-chain shocks. Analysts acquire skills through cross-brokerage learning and within-brokerage coaching. While brokerage firms do not assign tasks according to topic-specific skills, evidence based on the market reactions to recommendation updates and the information diffusion speed along the supply chain suggests that investors do recognize these skills. [SSRN link]

“Equal Employment Opportunity in Supply Chains” (with Ling Cen and Jing Wu) Accepted at Production and Operations Management 

Abstract:

The equal employment opportunity (EEO) policy is an essential component of workplace diversity, equity, and inclusion (DEI) practices. This paper examines whether principal customer firms infuse EEO policy in their dependent suppliers. Specifically, using a novel workplace EEO measure based on the textual analysis of online job postings, we demonstrate a lead-lag pattern of workplace EEO policies between customers and suppliers, suggesting that the suppliers adjust workplace EEO practices to cater to their principal customers. This effect is more pronounced when the principal customers enjoy a stronger bargaining power. To alleviate the endogeneity concerns, we use the adoption of the California Board Diversity Law in 2018 as the exogenous shock to support a causal interpretation of our findings. Furthermore, the supplier’s adverse workplace EEO incidents will increase the likelihood of supply chain relationship termination, especially when the customer’s workplace EEO level is high. At last, we find that higher customer workplace EEO levels boost suppliers’ innovation performance, measured by patent quantity and quality, suggesting economic benefits associated with the diffusion of workplace EEO practices along supply chains. [SSRN link]

“Data Scientists on Wall Street” (with Ling Cen, Bing Han, and Chanik Jo)

Abstract:

Financial institutions have significantly increased their recruitment of data scientists in the last two decades. We find that the number of data scientists employed by financial institutions causally affects their ability to earn abnormal profits. Data scientists' ability to generate abnormal profits on a stock is positively correlated to the concentration of data scientists across all institutional investors holding the stock. Institutional investors strategically adjust portfolio allocation and recruitment decisions to maximize the benefits generated by their data scientists. Consistent with the notion that the competition among data scientists speeds up the production and trade of private information, we also show that the concentration of data scientists covering a stock reduces its price informativeness in the capital market. [SSRN link]

“Artificial Intelligence Along the Supply Chain” (with Ling Cen, Jiaping Qiu, and Jing Wu)

Abstract:

We examine the supply chain as an important economic channel for the diffusion of artificial intelligence (AI) technologies. Leveraging a novel textual dataset of individual career histories, we capture the AI adoption by detailed recruitment records of employees with AI-related skills, and identify a lead-lag pattern in AI adoption between principal customers and their dependent suppliers. This pattern is specific to firm-level supplychain relationships and cannot be explained by industry-level links or market-wide trends. We pin down the causality by taking advantage of random outcomes in customers' H-1B visa lotteries. Consistent with a learning channel, we find a stronger diffusion of AI adoption along supply chains when there exist employees, particularly those with higher AI exposure, moving from customers to suppliers or when the geographical distances between customers and suppliers are shorter. The diffusion of AI technologies significantly improves suppliers' quality management and cost efficiency, highlighting positive externalities of AI investment along supply chains. Our findings have important implications for corporate managers and policymakers aiming to foster AI-driven innovation and operational excellence in supply chains. [SSRN link]

Awards & Honors

Best Paper Award, AsianFA Annual Conference, 2022

Best Paper Award (PhD Symposium), 12th FMCG Conference, 2022

Competitive Graduate Student Research Grant, The Chinese University of Hong Kong, 2022

Postgraduate Studentship, The Chinese University of Hong Kong, 2018-present

Outstanding Freshman Scholarship, Tsinghua University, 2014

Teaching

Stevens Institute of Technology

BT321 Corporate Finance, 2023

The Chinese University of Hong Kong

Teaching Assistant

Behavioral Finance, 2018-2022

- Teaching Evaluation Score: 6/6 (2022)

Mergers & Acquisitions (MBA course), 2018-2021