Zehua Zhang

Digital Economy Research: Conducted industry research, case analysis, and standards (Index) of digital technologies and digital economy. Completed multidimensional data analysis, project, and research report writing based on national strategic development. Developed China digital and innovation vitality evaluation system combined with ESG in terms of regional, industrial, and enterprise level.

Fintech Research: Assisting in writing academic papers, books, reports, and whitepapers related to Blockchain application, crypto assets investment, Web3.0, etc.

Cooperation, Consultation, and Academy Construction: Promoted and implemented university-enterprise business and academic cooperation. Provided consulting services with practical and academic value for enterprises and government departments, including Ministry of Commerce, People's Bank of China, and National Bureau of Statistics.

Data Analysis and Processing: Constructed backtesting system by Python and applied Multi-Factor Model to select stocks. Conducted statistical analysis and data visualization to model outcomes.

Strategy Formulation: Created new trading strategies by proposing factor combination on high-frequency trading in the Bitcoin market, greatly improving post-fee annualized returns compared to single factor method.

Forecasting: Constructed factor library through validity analysis with principal component analysis. Built new indicators and timeseries strategies based on historical stock trading data and applied both sentiment factor and LSTM model to predict stock price in the A-share market.

Data Processing and Analysis: Conducted the data acquisition and data pre-processing of a wide range of tokens transaction from decentralized exchange. Constructed the indexes, especially the potential arbitrage among the tokens. Calculated topological features for arbitrage network, performed data visualization including network mapping, chord diagram, and arbitrage distribution histogram, analyzed the database for static network.

Advanced Algorithms Application: Explored the application of Temporal Graph Neural Networks (TGNN) on continuous-time dynamic graphs represented as sequences of transactions which delivered as the dynamic prediction.

Arbitrage Strategies Discussion: Investigated cryptocurrency arbitrage trading strategies including token selection, short or long-trade between token pair, and triangular arbitrage transaction.

Domestic IaaS market research: Created a strategic plan for the future development of a financial market/data supplier. Led market research on data and algorithms of competitive products or solutions. Completed plan for financial IaaS technology, covering cost calculation and data management module for database construction.

Semiconductor Industry Analysis: Formed product development strategies for a domestic enterprise, including analyzing value chain, market size, main competitor, business model, etc. Wrote summary report on market domination strategy.

Investment Analysis: Participated in the allocation of 100 million dollars of investment in-depth, assisted in completing business and financial investigations, and conducted presentation through PPT.

Investment Portfolio Construction: Simulated the construction of the best investment portfolio in risk-free assets and venture capital portfolios, processed more than 2,000 pieces of data, and made Excel tables to display the results.

Risk control: Conducted cross-hedging using Hang Sheng Index Futures and funds construction to hedge systemic risk and was awarded the champion in the investment and hedge fund construction team competition.

Provided optional solutions built on the automatic market maker (AMM)-based decentralized exchange platform, and explored the potential pricing mechanism of Ethereum forward contracts on its platform.

Applied crawlers to obtain datasets from Kaggle and convert large-capacity datasets into NoSQL and MongoDB. Used R and Julia to model logistic regression and random forest, and performed comparative analysis to determine the relative impact of variables. Performed statistical analysis and visualization of stock trading data using Python and R.

Conducted literature review of multiple topics, including equity valuation, asset pricing, dedicated short bias, and financial markets.

Conducted the data acquisition and data pre-processing of a wide range of tokens transaction from decentralized exchange. Explored the application of Temporal Graph Neural Networks (TGNN) on continuous-time dynamic graphs represented as sequences of transactions which delivered as the dynamic prediction. Investigated cryptocurrency arbitrage trading strategies including token selection, short or long-trade between token pair, and triangular arbitrage transaction.