Research Consultant (Gold Level)
WorldQuant BRAIN
Total years of experience :1 years, 11 Months
Conducted in-depth reviews of the latest academic papers on financial markets, analyzed fundamental data of both the U.S. and Chinese markets, and developed long-short market-neutral quantitative alphas.
• Collaborated within an 8-member team to perform statistical event studies, leveraging data analysis to deliver macro market intelligence and real-time analysis reports. Efforts supported strategic decision-making and market positioning.
• Engineered and managed multiple medium and low-frequency equity quantitative long-short market-neutral strategies, achieving a Sharpe ratio of 2.5+. Utilized RandomSearch and Bayesian optimization for hyperparameter tuning, enhancing strategy performance.
• Applied machine learning models for the classification of all existing Alphas, employing correlation coefficients and Lasso regression among other methods to select high-efficacy ones from each category, improving model accuracy.
• Challenged 5+ existing alpha methodologies, proposing and accomplishing key improvements through parameter adjustments and rigorous backtesting, increasing strategy effectiveness in alignment with evolving market dynamics.
• Conducted comprehensive data analysis and visualization on a large-scale dataset of over 80, 000 patients, encompassing records of personal information and medication usage. Employed Python for data processing for insightful visualizations and constructed multinomial logistic model to predict patient choices among different medications.
• Implemented a Markov Decision Process framework to simulate competitive interactions among pharmaceutical suppliers, including defining key players (pharmaceutical manufacturers), states (market conditions), and actions (pricing decisions). The model predicted market trends with an accuracy of 76.34%.
• Utilized Deep Q-Network to address the MDP, employing reinforcement learning to find pricing strategies maximize long-term profits in a dynamic market environment, and Training using historical data, where network learned optimal actions guided by a reward function through trial and error.
• Kept abreast of market trends, risks, opportunities, and emerging investment products through diligent monitoring of the financial press. Analyzed financial statements and earnings prospects to refine existing investment strategies, ensuring alignment with market conditions and investment goals.
• Executed comprehensive quantitative and qualitative analysis, generating detailed valuation data and market reports to evaluate and summarize key metrics for investment opportunities, including portfolio companies, industry trends, and valuation benchmarks.
• Designed and performed 10+ innovative investment strategies, encompassing fundamental and event-driven approaches across diverse asset classes, including options, to improve trading outcomes.