Hui-Ching Chuang is an Associate Professor in the Department of Statistics at National Taipei University. Her research interests encompass machine learning applications, natural language processing, econometrics, and investment. [cv]

WORKING PAPERS

1. Limitation of Firm Fixed Effects Models and the Missing R&D-Patent Relation: New Methods and Evidence(with Po-Hsuan Hsu, Chung‐Ming Kuan, and Jui-Chung Yang) [ssrn] [slide][code] The SFS Cavalcade Asia-Pacific 2024; The 2024 UC Davis-FMA Napa Finance Conference; Max Planck I&E Seminar*; The 16th NYCU Finance Conference Keynote Speech*; The 2024 FMA Asia Pacific Conference**. (Presented by *Po-Hsuan Hsu)

   The common practice to include firm fixed effects in empirical research 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 that R&D input only positively explains patent output in half of prior regression estimations. This “missing link” can be attributed to the persistence of R&D and patents that causes the between-firm variation to be absorbed by firm fixed effects. We consider adjusted Hausman–Taylor estimates and advanced machine learning methods, and find that both methods lead to a clear positive R&D–patent relation. In particular, advanced machine learning methods suggest that only 10–20% of firm dummies are informative for the R&D–patent relation and that including other non-informative firm dummies may bias the identification. This paper thus offers two ready-to-use econometric methods to serve as a “second opinion” for empirical researchers working 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] European Financial Management Association 2025 Annual Meeting, Greece. (expected) Quantitative Finance Workshop 3: Asset Pricing and Risk Management, IMS, NUS, Singapore. The 26th Conference on the Theories and Practices of Securities and Financial Markets.

   This paper fine-tuned the FinBERT, a large language model (LLM) tailored for the financial domain, to classify hedge funds into systematic and discretionary styles. By leveraging advanced LLM techniques, our approach mitigates the subjective judgment traditionally involved in categorizing investment strategies. We find that on average, funds classified as systematic yield higher factor-adjusted returns than their discretionary counterparts. Moreover, after implementing a false discovery rate adjustment, we observe that between 10% to 20% of funds exhibit statistically significant positive alphas in models with either observable and/or unobservable 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)

   This paper applies machine learning and advanced natural language processing techniques to estimate the probabilities of commercial use of patents, over time at scale. We combine three surveys of inventors reporting on US patents as independently labeled training data, and use a combination of contextual embedding codings of the patent text (BERT for Patents) and bibliometric indicators from the patent documents to develop machine learning models for patented inventions.

4. Assessing Risk Spillovers with (Lasso) VAR for Expectile(with O-Chia Chuang, Zaichao Du, and Zhenhong Huang) The 28th Conference on the Theories and Practices of Securities and Financial Markets; NTU; TFA 2020; TES 2020; The 6th Annual Meeting of Young Econometricians in Asia-Pacific*. (Presented by *O-Chia Chuang)

   This paper extends the vector autoregressive (VAR) model for conditional means to VAR for conditional expectiles (MCARE) to assess the risk spillovers among multiple units. We further generalize MCARE to high-dimensional cases by imposing an L1-penalization (L-MCARE). As an empirical application, we apply MCARE and L-MCARE to the model the return network of global systemically important banks.

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.