This conference features one main forum and four parallel sessions. The main forum focuses on ‘current academic frontier issues in the fields of macrofinance and economics’, and has specially invited internationally renowned professors and economists to deliver keynote speeches: Sumit Agarwal, the Distinguished Professor at the National University of Singapore Business School and Director of the Sustainable and Green Finance Institute, Chair of the Asian Bureau of Financial and Economic Research (ABFER); Zhou Guofu, Chair Professor of Finance at the Olin Business School, Washington University in St Louis; Chen Hui, Nomura Chair Professor of Finance at the MIT Sloan School of Management and Research Fellow at the National Bureau of Economic Research; as well as over 20 experts and scholars from renowned universities both in China and abroad, who joined the faculty and students in attendance to discuss the latest academic developments and achievements.

Miao Jianjun, Qingshan Chair Professor at Zhejiang University, Dean of the School of Economics and AFR, delivered an opening speech on behalf of Zhejiang University and the conference organisers. He noted that the International Conference on Financial Economics has been held for fourteen consecutive years and has become a widely recognised platform for academic dialogue in the fields of economics and finance. This year’s conference has brought together leading scholars from around the world to engage in profound discussions on theoretical and empirical issues in macroeconomics and finance. He emphasised that the conference serves not only as an intellectual forum for exploring the frontiers of academic research, but also as a vital platform for grounding research in domestic practice, engaging in global dialogue, and enhancing China’s voice in the field of finance. He expressed his hope that, through this conference, original theories would be developed based on China’s financial practices, thereby establishing an ‘academic landmark’ with international influence; at the same time, he hoped it would build a bridge for mutual learning and exchange, serving as a ‘core hub’ for collaboration amongst universities nationwide, and jointly promoting the high-quality development of China’s financial disciplines.

In his opening speech, Professor Wen Yanbing, Vice-President of Jiaxing University, noted that over the past five years, the School of Economics at Jiaxing University had worked in close collaboration with partners such as the AFR at Zhejiang University and the Finance Institute of Zhejiang Province, sharing a common vision and working in aligned directions, and had together witnessed the increasing development of the ‘International Conference on Economics and Finance’ as an academic platform. Jiaxing University will continue to uphold the principles of openness, cooperation and sharing, further deepening strategic collaboration and resource sharing with all parties, whilst continuously improving the academic ecosystem. Working hand in hand with colleagues from all sectors, the University aims to develop the conference into a leading academic brand with significant international influence and distinct Chinese characteristics, thereby contributing further to the development of a financial discipline system with Chinese characteristics and enhancing China’s voice in global financial governance.

In his keynote speech entitled ‘Humans, Machines, and the Art of Regulation’, Professor Sumit Agarwal discussed human judgement, machine-assisted regulation and the design of regulatory frameworks. He pointed out that the challenges of regulation lie not only in the rules themselves, but also in how they are enforced: different regulatory bodies may produce inconsistent outcomes; a lack of information can lead to defensive regulation; and excessive discretion may amplify decision-making noise. Machines are not intended to replace regulators, but rather to be embedded within regulatory frameworks, serving to establish algorithmic benchmarks, provide hard data anchors and impose weighting constraints. The key to future regulation lies in human-machine collaboration: humans capture contextual and soft information, whilst machines provide consistency, transparency and discipline, thereby reducing systemic volatility whilst preserving regulatory flexibility.

Professor Zhou Guofu delivered a keynote speech entitled ‘Generative AI, Episodic Factors, and Patent Citation Networks’. He proposed a framework of episodic factors, challenging the assumption of constant and effective factors in traditional asset pricing, and revealed that the role of factors dynamically adjusts in response to changes in the economic environment, thereby enhancing the ability to forecast returns. He also introduced methods for processing corporate numerical features using large language models, promoting the deep integration of artificial intelligence with financial data analysis and improving portfolio performance. Furthermore, based on patent citation networks, he analysed knowledge diffusion between firms and found that indirect knowledge linkages possess a more enduring predictive power for future stock returns. In addition, by comparing market performance during periods when different political parties were in power in the United States using historical data, he pointed out that macroeconomic policy orientations have a greater influence on capital markets than party affiliations.

Professor Chen Hui delivered a keynote speech entitled ‘Teaching Economics to the Machines’. He pointed out that economic theory has not lost its value due to advances in data and computing power; on the contrary, it can serve as a priori knowledge, providing structured constraints for machine learning models, and plays a particularly important role in scenarios such as those with limited samples, high levels of noise, and out-of-distribution predictions. He systematically introduced the ‘theory-guided transfer learning’ framework, which involves first using economic theory to generate simulated data for pre-training neural networks, followed by fine-tuning with real data, thereby combining theoretical knowledge with empirical data. Through case studies such as option pricing, macroeconomic forecasting and multi-theory transfer learning, he demonstrated the method’s advantages in terms of predictive accuracy, robustness and generalisation ability. Research indicates that even if theoretical models are not entirely accurate, they can still enhance machine learning performance as long as they capture some of the underlying real-world patterns. He emphasised that the AI era does not mark the ‘end of theory’, but rather a new phase of collaborative innovation between theory and data.

The main venue was co-chaired by Vice-Dean Zeng Tao and Vice-Dean Hu Jin.

On 13 June, the conference hosted four concurrent thematic academic parallel sessions, focusing on ‘Macrofinance’, ‘Financial Econometrics’, ‘Asset Pricing’ and a ‘PhD Students’ Session’. Associate Professors Gong Xun, Zeng Tao and Xu Qi, and Research Fellow Wei Binru served as chairs of the respective sessions. In each session, participating scholars engaged in profound discussions on frontier topics. The atmosphere was one of intense academic engagement, with debates proving both vibrant and constructive. The scholars’ rigorous scholarly approach and their commitment to truth and practicality not only demonstrated the high standard of research within the field of finance but also provided valuable inspiration for the further development and expansion of future research.

Wang Xia, Professor at the School of Economics, Renmin University of China, delivered a keynote speech titled Three-Dimensional Factor Augmented Regularized Model. The presentation centered on high-dimensional forecasting problems in three-dimensional panel data. The study incorporates a global factor and two types of local factors simultaneously into a target variable forecasting framework, utilizing Principal Component Analysis (PCA) to extract latent common structures from multidimensional data, and combining it with the Adaptive Group LASSO method to address high-dimensional variable selection in forecasting equations. This model captures common fluctuations at the macroeconomic level while preserving local heterogeneity across different dimensions, providing a new econometric tool for dimensionality reduction, modeling, and forecasting of complex economic and financial data. The report demonstrates that against the backdrop of rapid growth in multidimensional data, the combination of factor models and regularization methods helps enhance forecasting accuracy and model interpretability.

Shi Zhentao, Professor at the Department of Economics, The Chinese University of Hong Kong, delivered a keynote speech titled Bagging the Network. The presentation systematically introduced estimation and inference problems in two-sided network formation models. Addressing network data that include individual fixed effects, the study proposes a unified estimation framework applicable to non-transferable utility networks. To tackle challenges such as high-dimensional fixed effects, two-sided consent mechanisms, and non-concave likelihood functions, the paper combines initial values from method of moments, Le Cam's one-step correction, and a split-network jackknife bagging method to effectively correct for incidental parameter bias while maintaining estimation efficiency. This research not only extends the theoretical methodologies of network econometrics, but also provides a new empirical tool for understanding network formation mechanisms in social relationships, risk sharing, wealth disparities, and group interactions.

Huang Naijing, Professor at the School of Economics, Central University of Finance and Economics, delivered a keynote speech titled From Micro to Macro: Learning Real-Time Economic Signals from Firm-Level Accounting Data. The presentation explored how to extract real-time macroeconomic signals from firm-level micro-accounting data. Breaking away from the traditional macroeconomic forecasting approach that relies on aggregate indicators, the study directly utilizes the financial information of more than 20,000 U.S. listed companies and leverages machine learning methods to perform real-time forecasting of total U.S. output. The results show that preserving firm heterogeneity and inter-firm information structures can significantly improve the accuracy of GDP nowcasting. This research indicates that firm-level operational data can not only reflect individual performance, but also serve as an important window for observing macroeconomic trends, providing a new data foundation for policy evaluation and market analysis.

Liu Cheng, Professor at the Economics and Management School, Wuhan University, delivered a keynote speech titled A Multi-Step Approach for Integrated Covariance Matrix Estimation with High-Frequency Data. The presentation focused on the problem of integrated covariance matrix estimation using high-frequency financial data. High-frequency trading prices are often contaminated by market microstructure noise, which may exhibit autocorrelation and cross-sectional correlation, causing traditional estimation methods to suffer from biases in finite samples. This study proposes a multi-step estimation approach that improves the finite-sample performance of pre-averaging methods in real data by jointly correcting the integrated covariance matrix and the long-run covariance matrix of the noise. The report provides a more refined econometric methodology for high-frequency risk measurement, portfolio management, and financial market stability analysis, demonstrating the practical value of high-frequency financial econometrics in real-world financial decision-making.

Liu Xiaobin, Associate Professor at Lingnan College, Sun Yat-sen University and AFR Researcher, delivered a keynote speech titled Seeing Sentiment: News Images, Retail Participation, and Chinese A-Share Index Returns. Breaking through the limitations of previous financial news research that heavily favored textual content, this study utilizes news articles and images from Sina Finance spanning from 2014 to 2026 to construct image pessimism and text pessimism indicators, respectively. The results show that both types of sentiment provide predictive information for short-term returns. However, this predictive power is not universal; instead, it is concentrated in the ChiNext market, where retail participation is higher and arbitrage constraints are stronger, and it is significantly amplified during high-volatility periods.

Shi Zhan, Associate Professor at the PBC School of Finance, Tsinghua University, delivered a keynote speech titled Yield Curve Arbitrage in the Credit Market. The presentation centered on the challenges of estimating issuer-specific yield curves in the corporate bond market, proposing a methodology that utilizes information from the bonds of similar firms to construct these curves. A relative value signal is then developed based on the deviation between market yields and the curve-implied yields. The report further analyzes the predictive power of this signal for future corporate bond returns, as well as its relationship with market mispricing, liquidity, and limits to arbitrage.

Gao Junxiong, Assistant Professor at the Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University, delivered a keynote speech titled Electoral Discipline, Economic Growth and Asset Prices. Based on Gallup presidential approval ratings, the study constructs an electoral pressure indicator to measure the strength of electoral discipline under different political states. The report examines the relationships between this indicator and U.S. economic growth, consumption growth, fiscal expenditures, political uncertainty, and stock excess returns. It further analyzes how electoral pressure influences the macroeconomy and asset prices by constraining government rent-seeking and fiscal behaviors.

Li Jiangyuan, Associate Professor at the School of Finance, Shanghai University of Finance and Economics, delivered a keynote speech titled The Zero-Sum Game of Mutual Fund Marketing. Setting the study in the context of mutual fund companies' social media live streaming, the research analyzes the impact of live-stream marketing on fund inflows and fund company revenues. The report finds that while live streaming increases fund inflows into the recommended funds, the overall capital and fee revenue of the fund management company do not increase simultaneously. This indicates that live streaming primarily drives the reallocation of capital within the same fund company. The study also analyzes fund companies' strategic use of live streaming for investor retention and market share defense.

Zhou Ti, Associate Professor at the School of Management and Economics, Harbin Institute of Technology (Shenzhen), delivered a keynote speech titled Implied Volatility Spreads and Stock Market Return Predictability in China: Informed Trading or Liquidity Demand? Focusing on SSE 50 ETF options, the study examines the informational content of the difference in implied volatility between call and put options. The report finds that the implied volatility spread negatively predicts future ETF returns at both weekly and monthly horizons, and further analyzes its relationships with the option-cash basis, liquidity demand, and trading pressure in the options market.

Zhu Yifang, Associate Professor at the School of Finance, Central University of Finance and Economics, delivered a keynote speech titled Beyond L1: Double L0 Regularization in Testing New Factors. The study addresses the problem of factor selection against the backdrop of an ever-increasing number of candidate factors in asset pricing. It introduces a double L0 regularization method within a stochastic discount factor (SDF) framework to identify pricing factors with cross-sectional explanatory power from a vast pool of candidate factors. The report further analyzes the application of this method in mitigating omitted variable bias, factor selection, and model comparison.

Wu Fenglin from the Shanghai Advanced Institute of Finance at Shanghai Jiao Tong University, Liu Shuaichen from the College and Graduate School of Arts & Sciences at the University of Virginia, Wang Huihui from the School of Economics at Zhejiang University, Lei Yuqin from the PBC School of Finance at Tsinghua University, Wu Yuezhi from the School of Management at Fudan University, Zhang Yichuan from the School of Government at Sun Yat-sen University, and Hou Xiyao from the School of Management at Zhejiang University delivered keynote speeches titled Common Idiosyncratic Volatility in Cryptocurrencies, Policy History, Coupon Stocks, and Mortgage Transmission at the ELB, Corporate Leverage Responses to Capital Controls: International Evidence, Refundable Tax Credits and Mortgage Credit Access: Evidence from State EITC Expansions, Housing Crash, Gold Rush, When the Sky Turns Transparent: Pollution Information Disclosure and the Scientists' Research Behavior, and Benchmark-Relative Tail Risk: An Axiomatic Theory with Applications, respectively.