Hong Kong Ph D

Siyuan Wu

+86-188******** +852-******** ************@*******.***

Education

Chinese University of Hong Kong Hong Kong SAR

Ph.D. in Finance Research Interest: Asset Pricing, Big Data, Institutions Sep. 2020 – (Expected) Jun. 2026 University of Texas at Austin Austin, U.S.

McCombs School of Business, Visiting Scholar Sep. 2024 – Jul. 2025 Renmin University of China Beijing, China

Master of Financial Engineering Major GPA: 3.8/4.0 Sep. 2017 – Jun. 2020 Xiamen University Xiamen, China

Bachelor of Financial Engineering Major GPA: 3.8/4.0 Sep. 2013 – Jun. 2017 Professional Experience

WorldQuant LLC China

External Quantitative Research Consultant Jul. 2016 – Sep. 2019

• Worked as an independent researcher and contributed more than 100 statistical arbitrage signals using internal datasets, mainly focused on the U.S. equity market. The majority of signals were based on academic papers.

• Ranked top 10 in the alpha challenge competition; selected for the inaugural mainland China summer internship and received professional training from experienced researchers. IDG Capital Beijing, China

Quantitative Research Intern Jul. 2017 – Sep. 2017

• Developed a customized back-testing framework and explored statistical arbitrage signals for the Chinese stock market. Applied machine learning methods (e.g., Random forest, Boosting, LSTM, CNN) to optimize signal performance and combination.

Selected Research Projects

Academic-oriented; Policy-oriented

Financial Market Related

Passive Investment and Intraday Trading Dynamics 2019 – 2022 Based on NBBO and intraday short-selling data from four major exchanges, document causal evidence that the passive investment revolution contributes to the shift of intraday liquidity dynamics toward J shapes. Informed trading migrates towards the closing session, pooling with uninformed traders to avoid being detected. A long- short portfolio based on short-selling distribution change earns a FF3 alpha of 80 bps per month.

Close Auction Mechanism: U.S. and Hong Kong 2020 – 2021 Construct real-time LOBs and auction order imbalance using administrative data provided by the NASDAQ/HKEX. Regression analysis indicates that the introduction of closing auction improves closing price quality but worsens intraday liquidity, and the impact is more prominent in the Hong Kong market. Announced order imbalance and the auction order type usage could robustly predict instruments’ future return in both markets.

Volatility Control Mechanism: U.S. and Hong Kong 2021 – 2024 Reconstructed tick-by-tick price movement from the NASDAQ/HKEX trading message. Detect the extreme price movement episodes and compare with the quote behavior of the triggered circuit breaker events. Estimate the tail risk via the EVT approach (POT-GPD) for each stock and evaluate the policy effect in a DID framework. Results show both VCM (HK) and LULD (US) mechanisms improve the ex-post liquidity provision, smooth the price jumps, but weakly reduce or even increase the ex-ante volatility due to the magnet effect.

Demand-based Asset Pricing 2022 – 2025

Adopt a structural IO model on institutional portfolios to estimate the characteristic-based demand function for each investor, then implement counterfactual analysis to quantify their price impact and examine the return predictability driven by flow. A case study in the Chinese stock market suggests that government purchases, on average, reduce the size premium by 13% and increase the value premium by 6% annually. Moreover, the purchase weakly improves the market information efficiency. NLP Related

Predict Corporate Policy: Integrating Structured and Unstructured Data 2022 – 2025 Develop a framework combining firms’ actions, balance sheet information, and narrative disclosure to predict corporate investment policy. Identify firm investment spikes based on a dynamic structural model and use labeled action for sample separation and model training. Transform the firm’s disclosure into word embedding and com- bine the accounting variables to train a DNN model. Model comparisons show that introducing narrative context could improve the corporate policy prediction, but quantitative information still dominates the explainability: an appropriate quantitative model significantly outperforms fine-tuned ChatGPT’s prediction.

Echoes of Inflation: CEO Early-Life Inflation Experience and Firm’s decision 2023 – 2025 Train conference-call-specific Word2Vec embeddings model from historical transcripts, and establish a data- driven and words-uniqueness-adjusted dictionary related to macro economy. Then construct the CEO inflation attention based on their words. Results show that managers who experience higher inflation in their formative years exhibit greater sensitivity to the inflation shocks, more actively manage liability, and stocks experience better performance during the high inflation uncertainty period. Financial Institution Related

STEM Talent and Hedge Fund Industry Evolution 2024 – 2025 Analyze the labor flows and quantitative investment evolution within the hedge fund industry that relies on billions of LinkedIn profiles. Document that hedge funds with a greater share of employees holding STEM backgrounds or quantitative skills achieve superior performance and faster asset growth. In contrast, funds unable to adopt quantitative strategies or attract quantitative talent are more likely to exit the industry, contributing to rising industry concentration.

Others

• Programming: Python, SQL, R, MATLAB, SAS.

• Course Modules: Stochastic Analysis, Continuous Time Financial Econometrics, Dynamic Programming, Statistical Machine Learning, Time Series Analysis

• Professional Competition: Ubiquant Quantitative Trading Competition, Annual Champion (2019): Six- round competition includes algorithm trading, market making, multi-factor investment, automatic Texas hold

’em game, and machine learning for rating prediction.

• Database: Bloomberg, Wind, CEIC, Reuters, Moringstar, WRDS, and various alternative databases.

• Selected Awards: SFS Cavalcade Asia-Pacific Best Paper Award (2024); CICF Best Paper Award (2023); National Mathematics Competition of Chinese College Students, First Prize of Fujian Province (2014); National Chemistry Olympiad of High School, National Prize (2012).

• Research Grant: CUHK Competitive Graduate Student Grant (2023, 20K HKD); Project investigator, Dalian Commodity Exchange Research Grant (2018, 100K RMB)

• Language: Chinese (Native), English (Fluent).

• Investment Experience: Personal trading experience in the stock, FX, and commodity markets; interested in monetary and fiscal policy research.

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