Yumeng Cui

Yumeng Cui

Ph.D Candidate

Research

Publications

Forecasting GDP Growth Rates Using Accounting Earnings: A Large Panel Microdata Approach

  • Management Science, 2026, Forthcoming; UTD 24，FT 50，ABS 4*，SSCI JCR Q1
  • With Yongmiao Hong (UCAS), Naijing Huang (CUFE), Yicheng Wang (PKU)
  • Abstract: Economists and econometricians typically use aggregate economic and financial variables for gross domestic product (GDP) prediction. However, aggregation often results in a loss of valuable information, diminishing key features such as heterogeneity, interactions, nonlinearity, and structural breaks. We propose a novel microforecasting approach, using large panel data of firm accounting earnings from corporate financial reports to forecast GDP. By employing machine learning methods, we can effectively exploit this large microlevel information set to achieve substantially more accurate GDP forecasts. Our findings highlight the advantages and potential of utilizing microlevel data for macroprediction, diverging from the conventional macroforecasting paradigm that relies on aggregate data to forecast macrovariables.
  • Paper link.

Do Asset Prices Help Predict Inflation? Evidence from Individual Stock Prices

  • Journal of Business & Economic Statistics, 2026, Forthcoming; ABS 4，SSCI JCR Q1
  • With Yongmiao Hong (UCAS), Naijing Huang (CUFE), Yicheng Wang (PKU)
  • Abstract: This paper revisits the predictive power of asset prices for inflation, focusing on individual stock prices rather than aggregate indices. Using a large panel data of firm-level stock prices and applying machine learning techniques, we demonstrate that individual stock prices significantly enhance the accuracy of inflation forecasts, particularly over medium- to long-term horizons and during periods of high inflationary and deflationary pressure. Compared to composite and industry-level stock indices, other aggregate asset prices, and Fama-French factors, individual stock prices contain valuable heterogeneous information, offering richer insights for inflation forecasting. These findings provide new empirical support for macro-finance theory, affirming the predictive value of asset prices from a micro-level perspective.
  • Paper link.

Working Paper

Inflation Forecasting Information in Chinese Stock Prices: Evidence from a Micro-Level Perspective

  • Revise and Resubmit in Economic Research Journal
  • With Naijing Huang (CUFE), Yongmiao Hong (UCAS), Yuqing Qi (CUFE)

Revisiting the Meese-Rogoff Puzzle: Evidence from Financial Intermediaries

  • With Yongmiao Hong (UCAS), Naijing Huang (CUFE), Yicheng Wang (PKU)

Conferences

International Conferences

  • The 13th World Congress of the Econometric Society (ESWC 2025), Seoul Korea, 2025
  • 2025 China Economics Society (CES) Dean Forum, Beijing China, 2025
  • Annual Conference of the International Association for Applied Econometrics (IAAE 2025), Torino Italy, 2025
  • Ninth PKU-NUS Annual International Conference on Quantitative Finance and Economics, Beijing China, 2025

Domestic Conferences

  • 2024 Young Economists Forum, Jinan China, 2024
  • The 2nd China Digital Economy Development Forum, Zhuhai China, 2024
  • The 4th Workshop on Big Data Econometrics Theory and Applications: Artificial Intelligence and Econometric Modeling, Xiamen China, 2024
  • The 6th Future Economists Forum, Beijing China, 2023
  • The 5th China Finance Academic and Policy Forum (2023), Beijing China, 2023
  • The 3rd China Macroeconomic Forecasting and Policy Forum and Economic Outlook Seminar, Xiamen China, 2023

Projects

As Principal Investigator

  • CUFE Postgraduate Students Support Program for the Integration of Research and Teaching (Grant No. 2025208).

As Participant

  • National Natural Science Foundation of China (NSFC General Program) “Macroeconomic Forecasting Based on Micro Data and Large Models”.
  • National Natural Science Foundation of China (NSFC Young Scholars Program) “Forecasting China’s Macroeconomic Risks Using Quantile Machine Learning Methods”.
  • National Key Research and Development Program of China “Climate Change Impacts and Adaptation in Major Belt and Road Countries”.
  • National Social Science Foundation of China “The Impact of RCEP on High-Quality Agricultural Development in China and Policy Responses”.
  • Asian Development Bank “Strengthening Sustainable Cooperation between Yunnan, Guizhou, and the Greater Mekong Subregion”.
  • World Economic Forum “China–Brazil Soybean Supply Chain Case Study”.
  • World Wide Fund for Nature “Research and Policy Suggestions for Pilot Regions on Green Commodity Value Chains”.
  • World Wide Fund for Nature “Research on the Development of Sustainable Trade in Agricultural, Livestock, and Forestry Commodities in China”.
  • World Wide Fund for Nature “Research on the Role of Leading Multinational Corporations in Promoting Global Green Commodity Value Chains”.
  • Paulson Institute “Green Value Chain Development and Trade Policy for China’s Soft Commodities”.
  • UK-China International Forest Investment and Trade Project (InFIT) “Promoting Biodiversity Conservation through Trade in Agricultural and Forestry Commodities”.
  • UK-China International Forest Investment and Trade Project (InFIT) “Building Green Value Chains through Traceability Systems”.