2025 Suzhou Finance Conference (SFC)

Programme

Hosts

International Business School Suzhou, Xi¡¯an Jiaotong-Liverpool University

School of Management at the University of Liverpool

Friday 27 June 2025

Suzhou, China

ABOUT THE CONFERENC

Welcome to The First Suzhou Finance Conference, hosted jointly by the International Business School Suzhou (IBSS) at Xi¡¯an Jiaotong-Liverpool University (XJTLU) and the School of Management at the University of Liverpool. This one-day conference, to be held on 27th June, 2025 in Suzhou, aims to create an opportunity to explore financial developments and innovations and provides a platform for insightful academic exchanges and collaborations. In particular, it brings together leading scholars to the latest developments in asset pricing.

VENUE

No.8 Chongwen Road

BS Building BS574

International Business School Suzhou (IBSS)

Xi¡¯an Jiaotong-Liverpool University (XJTLU)

Suzhou Dushu Lake Science and Education Innovation DistrictSuzhou, Jiangsu Province

China

XJTLU Wi-Fi CONNECTION

• Wifi Account: XJTLU
• User Name: wifi68
• Password: Wifi682025
• Wi-Fi connection is available during the Conference in XJTLU.

TEA BREAKS and LUNCH VENUE?

• Tea Breaks: IBSS Floor 5 (outside room BS 574)
• Lunches: East Dining Hall

CONFERENCEC COMMITTEE

Xuezhong (Tony) He? Finance, IBSS

Jiatao Liu ¡ª¡ª Finance, IBSS

Yajun Xiao ¡ª¡ª Finance, IBSS

Agenda

Organizers

Professor Xuezhong (Tony) He

Tony is a Professor of Finance at the International Business School Suzhou (IBSS), Xi¡¯an Jiaotong-Liverpool University (XJTLU). Before joining IBSS in 2022, he was a professor of finance at the University of Technology Sydney (UTS), and he had previously held academic roles at UTS and the University of Sydney. Tony holds two Ph.D. degrees, in Finance from UTS and Applied Mathematics from Flinders University. His research focuses on asset pricing, financial market modeling, market microstructure, and nonlinear dynamics in finance and economics. Tony has published over 60 papers in economics and finance journals, including Management Science, and Journal of Finance, 15 book chapters, with more than 6,000 citations. Tony served as Co-Editor of the Journal of Economic Dynamics and Control (2013¨C2022), and holds editorial roles in several other journals.

Dr Jiatao Liu

Jiatao is an Assistant Professor of Finance at the International Business School Suzhou, Xi¡¯an Jiaotong-Liverpool University (XJTLU). He received his Ph.D. in Finance from Bayes Business School (formerly Cass), City, University of London, an M.Sc. in Finance from Johns Hopkins University, and a B.S. in Business Administration from The Ohio State University. His research interests include theoretical and empirical asset pricing, information economics, textual analysis, and market microstructure, with a particular focus on how information is transmitted in financial markets.

Dr Yajun Xiao

Yajun is an Associate Professor of Finance at the International Business School Suzhou, Xi¡¯an Jiaotong-Liverpool University (XJTLU). He holds a Ph.D. in Finance (Dr. rer. pol) from Goethe University Frankfurt, a Diploma in Mathematics from the University of Kaiserslautern, and a B.S. in Engineering from Tongji University. Before joining XJTLU, he held academic positions at University College Dublin, the University of Technology Sydney, and the University of Freiburg.

His research focuses on asset pricing, market frictions, corporate finance, credit risk, and the interaction between the macroeconomy and the banking sector. He has published in journals such as the Journal of Financial and Quantitative Analysis, Review of Finance, Journal of Money, Credit and Banking, and the Review of Asset Pricing Studies.

Keynote Speaker

Professor Jun Pan

Jun is a Chair Professor of Finance and Chair of the Faculty Committee at the Shanghai Advanced Institute of Finance (SAIF), Shanghai Jiaotong University. She was previously the School of Management Distinguished Professor of Finance at the MIT Sloan School of Management. Her research interests include Chinese financial markets, FinTech, asset pricing, derivatives, and market microstructure. She has published over 20 papers in top academic journals such as Econometrica, Journal of Finance, Review of Financial Studies, and Journal of Financial Economics, with more than 16,600 citations as of early 2024. Professor Pan currently serves as Managing Editor of the Review of Finance and Associate Editor of the Journal of Finance. She is a Senior Fellow and Executive Committee Member at ABFER, and a former NBER Research Fellow. She holds a Ph.D. in Finance from Stanford University and a Ph.D. in Physics from New York University.

Presenters

Professor Shiyang Huang

Shiyang is a Professor of Finance at the University of Hong Kong. He joined the faculty in 2015 after receiving his Ph.D. in Finance from the London School of Economics. He also holds M.A. and B.A. degrees in Economics from Tsinghua University. Shiyang¡¯s research interests lie in financial economics, empirical asset pricing, and investor behaviour. His work has appeared in leading journals including the Review of Financial Studies, Journal of Financial Economics, Management Science, and the Journal of Economic Theory. Shiyang has received multiple best paper awards at major international conferences, including CICF, the Melbourne Asset Pricing Meeting, and the Paris December Finance Meeting. Professor Huang currently serves as Associate Editor for the Journal of Financial Markets, Journal of Economic Dynamics and Control, and several other journals.

Professor Junye Li

Junye is the Li Dasan Chair Professor of Finance at the School of Management, Fudan University. He received his Ph.D. and M.Sc. in Economics from Bocconi University in Milan, and holds a Master's degree in Systems Engineering from Beijing Jiaotong University. His research interests include empirical asset pricing, financial econometrics, financial machine learning, and macro-finance. His work has been published in leading journals such as the Review of Financial Studies, Management Science, Journal of Econometrics, Journal of Financial and Quantitative Analysis, and Journal of Banking & Finance, and has been cited over 650 times. He has received several research awards, including Best Paper Awards at the 2024 China FinTech Research Conference and the 2023 Paris December Finance Meeting.

Professor Kai Li

Kai is a Professor of Finance and Assistant Dean at the Peking University HSBC Business School (PHBS). He received his Ph.D. in Economics from Duke University in 2013, after earning an M.A. in Economics from Peking University and a B.A. in Finance from Shanghai Jiao Tong University. Professor Li¡¯s research covers macro-finance, asset pricing, sustainable finance, and the Chinese financial market. He has published in leading journals including the Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Journal of Monetary Economics, and Review of Finance. His work on green finance was recognized as a top-cited article in the Journal of Finance, and has received awards such as the WFA Best Paper in Empirical Finance and the Amundi-ESSEC ESG Award. He currently serves as Associate Editor for China Economic Quarterly, Asia-Pacific Journal of Financial Studies, and China Finance Review International.

Dr Pengfei Sui

Pengfei is an Assistant Professor of Finance at The Chinese University of Hong Kong, Shenzhen. He received his Ph.D. in Social Science (Economics) from the California Institute of Technology, and holds an M.A. in Quantitative Economics and a B.A. in Finance from Renmin University of China. His research interests include asset pricing, behavioral finance, and financial frictions. His work has been published in the Journal of Financial Economics and has received multiple Best Paper Awards, including honors at the China Financial Research Conference (CFRC) and the International Young Finance Scholars Conference.

Prof Xuewei (Aaron) Yang

Xuewei is a Professor of Finance at the School of Management and Engineering, Nanjing University. He received his Ph.D. and M.Sc. from Nankai University and his B.Sc. from Xidian University. He completed his postdoctoral training in Finance at the City University of Hong Kong. His research interests include financial innovation, asset pricing, investor behavior, credit risk, and stochastic modeling. His work has been published in leading journals such as the Journal of Finance, Review of Financial Studies, and Journal of Financial Economics, and many others. He is a recipient of the GARP Risk Management Research Award.

Dr Jia Zhai

Jia is a Senior Associate Professor in Finance at the International Business School Suzhou, Xi¡¯an Jiaotong-Liverpool University (XJTLU). She received her Ph.D. and M.Sc. in Finance from the University of Essex. Before joining IBSS, she held academic positions at the University of Salford and Ulster University in the UK. Her research interests include FinTech, machine learning, and applied financial econometrics. Her work has been published in journals such as Expert Systems with Applications, Decision Support Systems, Quantitative Finance, and European Journal of Finance.

Discussants

Professor Charlie Cai

Charlie is a Professor of Finance at the University of Liverpool Management School, UK. Before this, he held professorial positions at the University of Leeds and the University of Bradford. He received his Ph.D. with a focus on market microstructure and has since developed broad research interests in financial technology, asset pricing, corporate governance, and banking. His recent work explores FinTech in emerging markets, pricing anomalies across global markets, and systemic risk. His research has been published in journals such as the Journal of Accounting and Economics, Journal of Money, Credit and Banking, British Journal of Management, and Financial Management. He currently serves on the editorial board of Accounting and Finance and actively collaborates with academic, industry, and regulatory partners.

Dr Xin Chen

Xin is an Assistant Professor of Finance at the WeBank Institute of FinTech and Shenzhen Audencia Business School, Shenzhen University. He received his Ph.D. in Finance from the PBC School of Finance, Tsinghua University, and holds M.A. and B.A. degrees in Economics and Finance from the Central University of Finance and Economics. His research interests include behavioral asset pricing and macro-finance. His recent work has been accepted for publication in the Journal of Finance and Management Science.

Professor Xu Feng

Xu is a Professor of Finance at the College of Management and Economics, Tianjin University. He holds a Ph.D. in Management from Tianjin University. His research interests include financial big data, asset pricing, corporate finance, and financial applications of artificial intelligence. He has published over 30 papers in academic journals such as the Journal of Management Information Systems, Journal of Money, Credit and Banking, Journal of Economic Dynamics and Control, and Management World. He currently serves as Coordinating Editor for the Journal of Management Science and Engineering and Associate Editor for Finance Research Letters.

?

Professor Youwei Li

Youwei is a Professor of Finance at Hull University Business School, UK. He holds a Ph.D. in Financial Econometrics from Tilburg University and a Ph.D. in Mathematics from Lanzhou University. Before Hull, he held academic positions at Queen¡¯s University Belfast. His research interests include asset pricing, financial econometrics, market microstructure, quantitative finance, longevity risk, and sustainable finance. His work has been published in journals such as Management Science, Journal of Economic Dynamics and Control, Journal of Empirical Finance, Quantitative Finance, and European Journal of Operational Research. He currently serves as Associate Editor for journals including the European Journal of Finance, Finance Research Letters, and the International Journal of Finance & Economics.

?

Dr Qi Xu

Qi is an Associate Professor of Finance at the School of Economics, Zhejiang University, and a Research Fellow at the university¡¯s Academy of Financial Research. He obtained his Ph.D. in Finance from the University of Warwick in 2016 and joined Zhejiang University as a ZJU 100 Young Professor. His research interests include Empirical Asset Pricing, Empirical Corporate Finance, International Finance, and Financial Econometrics. His work has been published in journals such as the Journal of International Economics, Journal of Banking and Finance, Journal of International Money and Finance, Journal of Empirical Finance, Journal of Futures Markets, and the Pacific-Basin Finance Journal.

Abstract

Paper 1: Dynamic Currency Mispricing and Arbitrage Profits

Jiawei Hong, Fudan University

Junye Li, Fudan University

Lucio Sarno, University of Cambridge and

Centre for Economic Policy Research (CEPR),

We find that mispricing is prevalent in currency markets by employing a conditional latent factor model that effectively captures short-lived mispricing under general assumptions on systematic risk. An arbitrage portfolio based on currency mispricing can achieve an annualized Sharpe ratio exceeding 0.8, and excess returns that cannot be explained by commonly used observable factors. The results also suggest that: currency characteristics contribute more significantly to currency mispricing than macroeconomic fundamentals; mispricing is closely related to country-specific limits to arbitrage; and the currency arbitrage portfolio is related to shifts in global financial conditions.

Paper 2: The Carbon Risk Premium Revisited: The Role of Production Networks

Shubo Kou, Nankai University

Kai Li, Peking University HSBC Business School

Minghao Li, Peking University

Wu Zhu, Tsinghua University

This paper emphasizes the critical role of indirect carbon risk exposure transmitted through supply chain linkages in assessing carbon risk and its cross-sectional asset pricing implications. This channel has been overlooked in the burgeoning literature on the carbon risk premium (or ¡°greenium"). We develop a tractable general equilibrium model that incorporates input-output linkages, carbon emissions, and climate regulatory risks. Analytically, we demonstrate that the carbon risk premium is captured by two sufficient statistics: direct and indirect carbon risk exposure, with the latter representing the network effect. Guided by the model, we empirically measure both direct and indirect exposures and demonstrate a significant cross-sectional carbon risk premium after accounting for firms¡¯ indirect carbon risk exposure. Through a decomposition, we quantify that indirect carbon risk exposure accounts for the majority of the premium, surpassing the impact of direct carbon emissions. Furthermore, we provide strong evidence that climate regulatory risks propagating through supply chains help explain the premium.

Paper 3: Understanding the Valuation Gap between State-Owned and Non-State-Owned Enterprises

Yuanlan Cao, Nanjing University

Avanidhar Subrahmanyam, University of California at Los Angeles

Xuewei Yang, Nanjing University

Peng Zhu, Nanjing University

What explains valuation disparities between state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs)? We address this question using the Chinese stock market as our backdrop. Our study simultaneously considers a large number of economic hypotheses on why SOEs¡¯ and NSOEs¡¯ market valuations might differ. The findings suggest that differential distribution across industries only partially explains SOE/NSOE valuation differences. Profitability and its uncertainty emerge as the most significant influences on disparities, followed by liquidity and expected growth. After controlling for these influences, we find that valuation differences between SOEs and NSOEs become economically and statistically insignificant across industries. Our work provides evidence supporting the applicability of classical valuation theories in SOEs, which are often described as anomalously deviating from traditional models of value.

Paper 4: The Virtue of Probability in Machine Learning Portfolios

Yi Cao, Xi¡¯an Jiaotong-Liverpool University

Ao Yang, Xi¡¯an Jiaotong-Liverpool University

Jia Zhai, Xi¡¯an Jiaotong-Liverpool University

We propose a probability-based method for constructing machine learning portfolios and demonstrate a surprising ¡°virtue of probability¡±: portfolios sorted by predicted probabilities can achieve remarkable Sharpe ratios, even with simple-structured models. The superior performance stems primarily from the reduction of variance in stock selection error and the strong monotonicity between probability and true stock ranking. Our comprehensive empirical analysis of 95 years of US stock data shows that probability-sorted portfolios achieve significant out-of-sample gains (Sharpe ratio of 4.02 without transaction cost, 2.85 with 50 bps cost), substantially outperforming portfolios using predicted returns. The strategy generates significant returns unexplained by established risk factors, including both the Fama-French five-factor model and an extensive set of 22 factors underlying factor momentum. These findings remain robust across multiple machine learning models, various quantile definitions in long-short portfolio construction, and distinct market regimes.

Paper 5: Marketwide Memory

Constantin Charles, London School of Economics

Pengfei Sui, The Chinese University of Hong Kong, Shenzhen

We propose a novel measure that allows us to study memory associations in financial markets over the course of several decades. Using our measure, we construct memory-based beliefs and show that they can explain return expectations from surveys as well as higher-moment beliefs implied by stock options and the VIX. We also show that memory associations drive trading decisions of individual investors: when investors are more likely to recall a past positive (negative) trading experience with a stock, they are more (less) likely to repurchase that stock. Our measure builds on two well-established regularities of associative recall: similarity and interference. For each point in time, it captures the probability that a representative investor recalls past episodes of a stock. Without any further assumptions, our measure generates signature patterns of cued recall, such as the recency effect and the recall of past crises during extreme episodes. We validate our measure using actual recall patterns extracted from transcripts of corporate events, like earnings calls, and show that our measure predicts which historical periods are mentioned during these events. Overall, our results show that theories of human memory can be broadly applied in financial markets.

Paper 6: Sustainable finance under regulation

Shiyang Huang, The University of Hong Kong

Alexandr Kopytov, University of Rochester

We build a model analyzing optimal environmental regulation in the presence of socially responsible investors. Investors care about greenness of their portfolios but cannot fully resolve the pollution externality. Regulations, such as pollution tax and subsidies to clean firms, reduce dirty firms¡¯ size but also reshape firms¡¯ shareholder compositions. Under the regulations, dirty firms¡¯ shareholders become, on average, less averse to holding polluting shares, and hence, these firms are less willing to adopt green technology. We show that pollution can increase with regulation stringency. Optimal regulations do not always fully correct the externality and can deviate from the Pigouvian benchmark.