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. 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.

3. Machine Learning in Hedge Fund Classification: Systematic vs. Discretionary Strategies and Their Performance Implications(with Chung-Ming Kuan) [ssrn]. 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 applies machine learning to classify hedge funds into systematic and discretionary categories. Leveraging textual analysis and machine learning methods, our approach eliminates subjective judgment in analyzing investment strategies. We find that systematically classified funds, on average, yield higher excess returns than discretionary ones. Additionally, after applying the false discovery rate test for linear asset pricing models, a higher portion of positive alpha is observed in the systematic category. The alpha average for outperforming systematic funds surpasses that of discretionary funds across various risk factor models.

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 list 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.