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Workshop Schedule

Next workshop

Our next speaker is Yuan Zhang from University of Texas at Dallas. Her personal website is https://profiles.utdallas.edu/yuan.zhang2.

The seminar will take place on: May 25, 2022, 9:00am-10:30am, Beijing Time (GMT+8) / May 24, 2022, 6:00pm-7:30pm, PST / May 24, 2022, 9:00pm-10:30pm, EST.

The title and abstract of the paper is as follows:

The Bright and Dark Side of a News Paywall

We examine the implications of a news paywall for the capital market by exploiting the adoption of a digital subscription model by Caixin.com, a major financial media outlet in China as a quasi-experiment. Using a difference-in-difference research design, we find that news articles at Caixin.com have more original and detailed content and have higher associations with future earnings news after the paywall implementation than before, compared to control media outlets. However, news articles behind the paywall are associated with higher bid-ask spreads relative to other news articles. Further analyses show the paywall leads to a decrease in retail investors’ activities on social media, but an increase in forecast accuracy by analysts who are more likely to subscribe to Caixin.com. Overall, we provide novel evidence that a news paywall improves news quality but also contributes to an unlevel playing field for investors.

Register for CBRN seminars: http://hk.mikecrm.com/4GHAgZg.

Past workshops

Eighth workshop

The speaker for our eighth workshop was Eric Zheng from University of Texas at Dallas. His personal website is https://personal.utdallas.edu/~ericz/.

The title and abstract of the paper is as follows:

The Mercy of AI: Combating Natural Disasters through Lending

Natural disasters wreak economic damage and cause financial distress to victims. However, when those in need of money apply for loans, lending firms often tighten up their lending practice as a reaction to victims’ impaired repayment ability. In this study, we show how lenders’ use of AI can help those who suffer from natural disasters. Collaborating with a leading credit scoring company in China that offers AI-as-a-Service (AIaaS) for lenders, we are able to observe lenders’ use of AI services in assessing the risk of loan applications. Through a Stochastic Frontier Analysis, we find that lenders who use AI services fare better in reducing the default rate of applicants suffering from natural disasters. Notably, such a default reduction effect is more pronounced for underprivileged borrowers with lower credit scores. We explore possible mechanisms under play and discuss the societal implications of our findings.

Seventh workshop

The speaker for our seventh workshop was Yanhui Wu from University of Hong Kong. His personal website is http://www.yanhuiwu.com/.

The title and abstract of the paper is as follows:

Social Media and Government Responsiveness: Evidence from Vaccine Procurements in China

This paper studies whether and how public opinion on social media affects local governments’ procurement of vaccines in China from 2014 to 2019. To identify causal effects, we exploit the regional variation in the eruption of social-media opinion on vaccine safety following sudden vaccination scandals. We find that governments in cities exposed to more intensive social media discussion increased the frequency and share of the more-transparent (open-bidding versus private arrangement) format of procurement for vaccine-related products. The effects are robust when we use early-stage social media penetration in China as an instrumental variable. Moreover, the effects are larger in cities where governments adopted more surveillance technology, the information environment was more strictly controlled, and local officials had stronger career concerns. Our overall findings shed light on the mechanisms and limitations regarding the effect of social media on policy compliance and government accountability.

Sixth workshop

The speaker for our sixth workshop was Hong Ru from Nanyang Technological University. His personal website is https://hongru.mit.edu/hong-ru-%E8%8C%B9%E5%BC%98.

The title and abstract of the paper is as follows:

Government Credit and Industrial Pollution

Using firm-level pollution data from Environmental Survey and Reporting Database in China and loan data from the China Development Bank (CDB), we analyze how government-subsidized credit from the CDB affects firm pollution activities from a supply chain perspective. Consistent with prior studies, we find that while SOEs in China produce substantially more pollution (e.g., emissions of chemical oxygen demand (COD), sulfur dioxide (SO2), ammonia-nitrogen (NH3-N), wastewater discharge, and waste gas emission) than private firms, CDB credit helps reduce the pollution from SOEs much more than the reductions from private firms. Moreover, we find that CDB credit to strategic industries at the top of the supply chain is associated with lower pollution levels for firms in downstream industries. We further shed light on the mechanisms underlying such CDB credit positive spillovers across the supply chain through emission abatement actions and outcomes. First, we find that CDB loans induce SOEs to use substantially less water and coals in their production than private firms. In particular, one standard deviation increase in CDB direct loan (upstream loan) is associated with an additional 2.35% (4.79%) reduction in freshwater usage and 8.4% (3.48%) decrease in coal usage compared to private firms. Second, the results suggest that CDB loans enable SOEs to reduce COD pollution through better treatment of COD {COD treatment/(COD treatment+COD emission)} compared to private firms. Our findings suggest the important role of government credit in reducing pollution across the supply chain.

Fifth workshop

The speaker for our fifth workshop was Bohui Zhang from The Chinese University of Hong Kong, Shenzhen. His personal website is https://sites.google.com/site/bohuizhang/.

The title and abstract of the paper is as follows:

Knowledge is Power: A Field Experiment in Chinese and US Stock Markets

This study examines the causal impact of financial knowledge on stock pricing efficiency. We created an investor education website and conducted a field experiment by providing knowledge about the pricing implications of accounting accruals to investors in randomized stock groups via social media in both China and the US. We find that treatment stocks experience a reduction in accrual mispricing relative to control stocks, and that the effect is most pronounced when both conceptual and methodological knowledge about accruals is provided. The education effect is stronger among stocks with investors who are either less sophisticated or can study the knowledge more intensively. Finally, we document a real effect of financial knowledge on firms. Treatment firms experience a decline in discretionary accruals in the post-experiment period, especially when they are heavily owned by individual investors or less monitored by institutions.

Fourth workshop

The speaker for our fourth workshop was Yue Zhang from Westlake University. His personal website is https://frcchang.github.io/index.html.

The title and abstract of the paper is as follows:

Recent progress and challenges in machine learning for Natural Language Processing (NLP)

In recent years, artificial intelligence has received increasing attention. Deep learning technology has enabled intelligent systems to perform tasks that are far more complex compared to what machines could do a decade ago. Investigating automatic understanding and generation of natural language texts, natural language processing has been a central topic of artificial intelligence since its dawn, and human language dialogue capabilities has been recognised as a major metric for evaluating artificial intelligence. Advances in the field allow intelligent systems to perform automatic speech to speech translation, question answering, essay scoring, automatic auditing and algorithm trading by news reading.

The field of natural language processing has evolved since the early days of computer science, going through three main stages, where rule-based methods, statistical methods and deep learning methods dominate the literature, respectively. Researchers and engineers have seen a shift from linguistic feature engineering to parameter tuning in their daily work as the state-of-the-art approaches shift from statistical learning to deep learning. Today, deep learning not only allows natural language processing systems to perform much better on existing tasks such as syntactic parsing and automatic machine translation, but also enables new tasks to be investigated.

This talk aims to introduce the field of natural language processing from a machine learning perspective, laying out the mathematical and algorithmic foundations for the major technologies of this field. The organisation follows the order of increasing complexity, which is also largely consistent with the development history of NLP technologies. In particular, an overview of major NLP tasks is introduced first, and the remaining talk evolves around machine learning methods.

Third workshop

Our third speaker was Kenneth Huang from National University of Singapore. His personal website is https://sites.google.com/site/kennethhuang/. The title and abstract of the paper is as follows:

Trade Protection and Firm Innovation: Impact from U.S. Anti-Dumping Sanctions on Innovation Output in China

This study examines how trade protections, such as anti-dumping sanctions on foreign exports, impact innovations developed by affected foreign firms. Using data on U.S. anti-dumping sanctions levied on Chinese exports from 1985 to 2015 and domestic patents developed by Chinese firms in China that are associated with the sanctioned products, we found that anti-dumping sanctions led to an increase in the overall number of domestic patents in China in technology classes that were most relevant to the sanctioned products. This result is consistent with our theory that anti-dumping sanctions enlarge the gap between the pre-innovation and post-innovation rents, thereby providing greater incentives for sanctioned firms to innovate. Furthermore, this effect was boosted by a major national-level pro-innovation campaign instituted by the Chinese central government to promote domestic innovations that further increased the post-innovation rent. However, we also found that anti-dumping sanctions decreased the production of novel patents in China that were most relevant to sanctioned products, which is consistent with the theory that anti-dumping duties reduce the resources available to firms to develop innovations. To sum up, these findings suggest that affected Chinese firms produce more innovations to escape competition and future sanctions, but that they can go only so far to produce more incremental innovations than novel innovations due to resource constraints. This study addresses the literature gap concerning how trade protections affect sanctioned firms’ innovation and generate important strategy and policy implications from such protections’ unintended consequences.

Second workshop

Our second speaker was Yongxiang Wang from Chinese University of Hong Kong (Shenzhen). His personal website is https://sites.google.com/view/yongxiangwang. The title and abstract of the paper is as follows:

The Chinese Collectibles Bubble

Using novel, hand-collected data from the largest Chinese collectibles exchange, we examine an asset price bubble in the collectibles market in the 2010s. Because the securitized collectibles were traded outside of the exchange, the fundamental price of the securities was publicly observable. This feature, combined with plausibly exogenous shocks to the cost of information acquisition, barriers to arbitrage, Tobin tax, and maturity allows us to further examine bubble theories in ways typically possible only in laboratory experiments. Our results are broadly consistent with resale-option theory, the importance of limited arbitrage, and provide support for the external validity of several key findings of the experimental bubble literature.

First workshop

Our first speaker was Clive Lennox from University of Southern California. His personal website is https://www.marshall.usc.edu/personnel/clive-lennox. The title and abstract of the paper is as follows:

The past 25 years have seen an exponential growth in the number of China studies in the leading accounting journals. The rise in China-related research mirrors the country’s increased importance on the global stage. We organize our review of the China literature around three central themes: 1) political and regulatory forces, 2) China’s relations with the outside world, and 3) the availability of novel data and regulatory shocks. The former two themes address research questions that are more China-centric, while the third exploits the China setting to examine questions that are less China specific. We highlight the contributions that China studies have made to the broader accounting literature, the limitations of the current literature, and we offer suggestions for future research directions.