It was during my Financial Numerical Analysis course that I was first introduced to quantitative fundamental analysis. This advanced research method can systematically assess equities' performance and potential value by integrating fundamental financial analysis with quantitative modeling techniques. I was amazed by the robust application of quantitative analysis in the stock markets and determined to explore this advanced and interdisciplinary method further.
在金融数值分析课程中，我第一次接触到了定量基本面分析。这种先进的研究方法通过将基本面金融分析与定量建模技术相结合，可以系统地评估股票的表现和潜在价值。我对定量分析在股票市场中的强大应用感到惊讶，并决心进一步探索这种先进的跨学科方法。
On the one hand, I immersed myself in fundamental analysis theories related to asset pricing and risk control in my investment course, particularly within the frameworks of the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). On the other hand, in Big Data and Financial Computing, I leveraged quantitative methods in Python to analyze the volatility characteristics of the Chinese A-shares stock market. Specifically, I used the Value-at-Risk (VaR) model and the Generalized Pareto Distribution (POT) to analyze the tail risks of over 1,000 listed companies and assessed the risk levels of the market under extreme fluctuations. These experiences solidified my academic foundation in quantitative fundamental analysis and reinforced my belief in accurately evaluating investment risks and returns.
一方面，我在投资课程中沉浸于与资产定价和风险控制相关的基本分析理论，尤其是在资本资产定价模型（CAPM）和套利定价理论（APT）的框架内。另一方面，在《大数据与金融计算》课程中，我利用 Python 中的定量方法分析了中国 A 股股票市场的波动特征。具体来说，我使用风险价值（VaR）模型和广义帕累托分布（POT）分析了1000多家上市公司的尾部风险，并评估了市场在极端波动下的风险水平。这些经历夯实了我在定量基本面分析方面的学术基础，坚定了我准确评估投资风险和收益的信念。
To further enhance my grasp of quantitative techniques, I joined a research project titled Quantitative Analysis of Investment Returns and Risks in China’s A-Shares. As a research assistant, I assisted with identifying and integrating robust quantitative factors to forecast equity performance. Using Python, I helped merge key financial metrics, industry-specific indicators, and corporate governance as factors into a cohesive forecasting model. We then refined the factor selection by calculating the Information Coefficient (IC) and Information Ratio (IR) and ultimately crafted an impactful industry momentum model tailored for the technology sector. This hands-on experience also led me to explore asset pricing dynamics through the Fama-French three-factor model and to simulate volatility in A-share returns using the GARCH (generalized autoregressive conditional heteroskedasticity) model. These inspiring efforts sharpened my analytical skills and fueled my passion for applying quantitative techniques to solve complex investment challenges.
为了进一步提高我对量化技术的掌握，我参加了一个名为 "中国 A 股投资回报与风险的量化分析 "的研究项目。作为研究助理，我协助识别和整合稳健的量化因子，以预测股票表现。我使用 Python，帮助将关键财务指标、特定行业指标和公司治理作为因子合并到一个有凝聚力的预测模型中。然后，我们通过计算信息系数（IC）和信息比率（IR）完善了因子选择，最终为技术行业量身打造了一个具有影响力的行业动量模型。这些实践经验还引导我通过法马-法兰克三因子模型探索资产定价动态，并使用 GARCH（广义自回归条件异方差）模型模拟 A 股回报的波动性。这些令人鼓舞的努力磨练了我的分析技能，激发了我应用量化技术解决复杂投资难题的热情。
Building on my academic experience, I secured an internship at the Overseas TMT (Telecommunication, Media, Technology) Group of Industrial Securities Research Institute. Guided by my supervisor, I focused on improving the accuracy and efficiency of fundamental analysis by applying data analytics techniques. I began by consolidating and scrutinizing quarterly financial statements from numerous companies, zeroing in on critical metrics such as revenue, net profit, and gross margin. Using data visualization tools such as Tableau and pivot tables, I created automatic dashboards that helped the team quickly grasp data trends and make informed decisions. Next, I employed time-series analysis to process market data for the TMT sector. I could forecast industry performance more precisely by calculating moving averages and constructing regression models. These efforts culminated in a 10% improvement in the time efficiency of our financial data analysis pipeline. Additionally, I supported my supervisor in identifying three new companies with significant growth potential, providing actionable insights for the team's investment recommendations.
在学术经验的基础上，我获得了兴业证券研究所海外 TMT（电信、媒体、科技）小组的实习机会。在导师的指导下，我重点运用数据分析技术提高基本面分析的准确性和效率。我首先整合并仔细研究了多家公司的季度财务报表，将重点放在收入、净利润和毛利率等关键指标上。利用 Tableau 和数据透视表等数据可视化工具，我创建了自动仪表盘，帮助团队快速掌握数据趋势并做出明智决策。接下来，我采用时间序列分析来处理 TMT 行业的市场数据。通过计算移动平均值和构建回归模型，我可以更精确地预测行业表现。通过这些努力，我们的金融数据分析管道的时间效率提高了 10%。此外，我还协助主管发现了三家具有巨大增长潜力的新公司，为团队的投资建议提供了可操作的见解。
My professional and academic experiences solidified my understanding of how quantitative fundamental analysis can drive meaningful investment outcomes. They have left an indelible mark on my approach to business analytics and prepared me well for a future career. After graduation, I aspire to become a quantitative analyst at an investment research institute on the West Coast. I aim to focus on emerging technology companies and enhance traditional analysis by constructing robust quantitative factor systems and developing data-driven forecasting models, thereby actively contributing to the broader application of quantitative fundamental research methods.
我的职业和学术经历巩固了我对定量基本面分析如何推动有意义的投资结果的理解。这些经历在我的商业分析方法上留下了不可磨灭的印记，并为我未来的职业生涯做好了充分准备。毕业后，我希望成为西海岸一家投资研究机构的量化分析师。我的目标是专注于新兴科技公司，通过构建稳健的量化因子系统和开发数据驱动的预测模型来加强传统分析，从而为量化基本面研究方法的广泛应用做出积极贡献。
Paul Merage’s MSBA program will facilitate my career development. The Data and Programming for Analytics course will provide a robust foundation in Python programming and data manipulation, which is essential for handling complex financial datasets and automating data-driven processes. Additionally, I expect to obtain advanced statistical techniques and machine learning algorithms to develop predictive models to forecast market trends and optimize investment strategies by attending Mastering Predictive Analytics. The integration of these courses aligns with the industry's demand for analysts who can bridge the gap between data science and financial decision-making.
Paul Merage 的 MSBA 课程将促进我的职业发展。分析数据和编程课程将为 Python 编程和数据操作打下坚实的基础，这对于处理复杂的金融数据集和实现数据驱动流程自动化至关重要。此外，我希望通过学习 Mastering Predictive Analytics 课程，掌握先进的统计技术和机器学习算法，从而开发预测模型，预测市场趋势，优化投资策略。这些课程的整合符合业界对能够在数据科学和金融决策之间架起桥梁的分析师的需求。
Paul Merage’s career resources are also highly advantageous for aspiring quantitative investment analysts. I am attracted by the ProSeminar courses and polish my skills in resume building, interview preparation, and networking strategies. Additionally, UCI’s extensive alumni network offers invaluable opportunities to engage with potential employers and gain insights into the latest industry trends and job opportunities.
Paul Merage 的职业资源对于有抱负的量化投资分析师来说也非常有利。我被 ProSeminar 课程所吸引，这些课程让我在简历制作、面试准备和人际关系策略方面的技能更加完善。此外，加州大学洛杉矶分校广泛的校友网络也为我提供了宝贵的机会，让我可以与潜在雇主接触，深入了解最新的行业趋势和工作机会。