Hui-Ching Chuang

Hui-Ching Chuang

About Me

Hui-Ching Chuang is an Associate Professor in the Department of Statistics at National Taipei University. Her research interests include econometrics and investment, with a focus on applying machine learning and natural language processing techniques.[cv]

WORKING PAPERS

  1. Revisiting the Missing R&D-Patent Relation: Challenges and Solutions for Firm Fixed Effects Models (with Po-Hsuan Hsu, Chung-Ming Kuan, and Jui-Chung Yang)[ssrn][slide][code]
    The SFS Cavalcade Asia-Pacific 2024; 2024 UC Davis-FMA Napa Finance Conference; Max Planck I&E Seminar*; 16th NYCU Finance Conference (Keynote)*; 2024 FMA Asia Pacific Conference. (*Presented by Po-Hsuan Hsu)
    Abstract
    The common practice of including firm fixed effects in empirical researzch may eliminate the explanatory power of important economic factors that are persistent. We use the intuitive R&D–patent relation to illustrate this point. Our review of recent studies suggests a surprising pattern: R&D input positively explains patent output in only half of prior regression estimations. This “missing link” can be attributed to the persistence of R&D and patents, which allows between-firm variation to be absorbed by firm dummies. We consider adjusted Hausman–Taylor estimates and advanced machine-learning methods, both of which restore a clear positive R&D–patent relation. Notably, ML models reveal that only 10-20 % of firm dummies are informative; including the rest biases identification. The paper offers two ready-to-use econometric “second opinions” for researchers dealing with explanatory variables that strongly correlate with between-individual unobservables.
  2. Classifying Hedge Fund Strategies with Large Language Models: Systematic vs. Discretionary Performance (with Chung-Ming Kuan)[paper][slide]
    European Financial Management Association 2025 Annual Meeting, Greece; Quantitative Finance Workshop 3 (Asset Pricing & Risk Management), IMS-NUS, Singapore; 26th Conference on the Theories and Practices of Securities and Financial Markets.
    Abstract
    We fine-tune FinBERT, a finance-specific large language model, to classify hedge funds as systematic or discretionary. Removing manual subjectivity yields cleaner style labels and reveals that systematic funds, on average, generate higher factor-adjusted returns than discretionary funds. After a false-discovery-rate adjustment, 10-20 % of funds still show statistically significant positive alphas in models that include both observable and latent risk factors.
  3. What Share of Patents Is Commercialized? (with Po-Hsuan Hsu, You-Na Lee, and John P. Walsh)
    TPRI Brownbag Seminar*; NBER Productivity Seminar*; Max Planck I&E Seminar*; TES 2023; Academia Sinica; NTU; NTPU; YZU. (*Presented by John P. Walsh)
    Abstract
    Leveraging three independent inventor surveys for ground-truth labels, we combine BERT-for-Patents embeddings of each patent’s text with detailed bibliometric variables (citations, assignee type, technology class, etc.) to train machine learning models that assign every U.S. patent a probability of commercial exploitation. This scale-able estimator uncovers previously unobservable commercialization patterns across technologies and filing cohorts, enabling finer analysis of the factors that drive patented inventions to the marketplace.
  4. Assessing Risk Spillovers with (Lasso) VAR for Expectile (with O-Chia Chuang, Zaichao Du, and Zhenhong Huang)
    28th Conference on the Theories and Practices of Securities and Financial Markets; NTU; TFA 2020; TES 2020; 6th Annual Meeting of Young Econometricians in Asia-Pacific*. (*Presented by O-Chia Chuang)
    Abstract
    We generalize the vector autoregressive (VAR) model from conditional means to conditional expectiles (MCARE) for assessing risk spillovers among multiple entities. For high-dimensional systems, we impose an L1 penalty (L-MCARE). Applied to the return network of global systemically important banks, MCARE and L-MCARE uncover time-varying tail-risk transmission patterns.

PUBLISHED PAPERS

  1. Jui-Chung Yang, Hui-Ching Chuang, and Chung-Ming Kuan (2020).“Double Machine Learning with Gradient Boosting and Its Application to the Big N Audit Quality Effect.” Journal of Econometrics, 216 (1), 268-283.

  2. O-Chia Chuang, Hui-Ching Chuang, Zixuan Wang, and Jin Xu (2024).“Profitability of Technical Trading Rules in the Chinese Stock Market.” Pacific-Basin Finance Journal, 84, 102278.

  3. Yin-Siang Huang, Hui-Ching Chuang, Iftekhar Hasan, and Chih-Yung Lin (2024).“Search Symbols, Trading Performance, and Investor Participation.”International Review of Economics and Finance, 92, 380-393.

  4. Yin-Siang Huang, Hui-Ching Chuang, Iftekhar Hasan, and Chih-Yung Lin (2021).“The Effect of Language on Investing: Evidence from Searches in Chinese versus English.”Pacific-Basin Finance Journal, 67, 101553.

  5. Hui-Ching Chuang and Chung-Ming Kuan (2020).“Identifying and Assessing Superior Mutual Funds: An Application of the New Step-wise Data-Snooping-Bias-Free Test.”Review of Securities & Futures Markets, 32 (1), 1-32.

  6. Hui-Ching Chuang and Chung-Ming Kuan (2010).“Testing the Performance of Taiwan Mutual Funds Based on the Tests without Data-Snooping Bias.”Review of Securities & Futures Markets, 22 (3), 181-206.

  7. Hui-Ching Chuang and Jauer Chen (2023).“Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles.”Econometrics, 11 (6).

  8. Hui-Ching Chuang and Jui-Chung Yang (2022).“Dynamic Panel Data Estimators in Leverage Adjustments Model.”Advances in Financial Planning and Forecasting, 10, 67-111.