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Mingzhe Cong – Personal Statement


MingzheCong – Personal Statement

Motivationthrough personal experiences plays a significant role in theattainment of personal and professional goals. My engagement andresearch about the stock markets and funds sparked my interest topursue a Master’s Degree in Quantitative &amp ComputationalFinance. In early 2015, I started to research on Chinese equitymarkets and later began to invest in stock funds using my salaries.Besides, I learned from other people about their experiences andperspective towards investing in the stock market. The Chinese stockbull market was a significant experience as I learned how people getirritated and emotional. My colleagues, despite their limitedknowledge and expertise, discussed the significance of investing inthe stock market. I also got motivation from the owners of downstreamfactories, as they would take a significant portion of their money toenterprise in the stock markets. Every day, financial journalists andequity analysts offer insightful information on the radio and CCTVnews about the opportunities of investing in stock. At that time, mycolleagues, factories’ owners and I were happy with the growingnumber of bank accounts.

Lateron, we became victims of the stock market bubble bursts after theShanghai composite index decreased by 48.15 percent in the fewsubsequent months. During the period, the news highlighted how peoplelost their life savings, pension, and asset-based loans. Some of thevictims committed suicide out of frustration due to severe financiallosses and high debt. The unusual fluctuations in China capitalmarkets gave me a lesson about how harmful and irrational the marketscould be to small-scale investors. Since then, I have been lookingfor quantifiable and scientific methods when making trading plans.Through the unintended opportunity, I took Quantitative &ampComputational Finance via an open course, Computational Investing,Part I, which is taught by Professor Tucker Balch. He is based atthe College of Computing, in the Georgia Institute of Technology.

Thecourse in Quantitative &amp Computational Finance will equip me withessential knowledge on statistics method, algorithm trading, andsolid theory that contribute towards understanding the pricemovements. I selected MIT, Yale, and Chicago Universities’quantitative finance open courses and using Quantopian/Join Quant,which offers investment market history data and Python platformneeded to develop a trading algorithm, as well as to develop tradingstrategies. As I studied the course intensely, I came to realize thatmy knowledge structure could not adapt to the finance industrydemand. Therefore, I decided to go back to the university to getsystematic training, as it would provide me with the right knowledgeand skills.

Aftergraduation, I would like to work in leading hedge funds, investmentbanks, or investment management corporations. I seek to utilize theknowledge on Quantitative Analysis, Strategic Analysis &amp Modelingusing integrated modeling, programming and financial skill to helpcompanies and customers gain stable long-term revenue and minimizerisk exposure. In the future, I would like to use the skills learnedfrom school and industry to design monetary products for averagecustomers with the aim of assisting them to avoid unexpected fiscalmarket fluctuations, enjoy their life, and benefit from globaleconomy development.

Inundergraduate school Purdue University, I developed broad interestbeyond my major whereby, I took economics and management as minorcourses. At the Carnegie Mellon University, I took a specialty onprocess system modeling. Besides, I gained skills in the numericalmethod, modeling, and optimization. I passed with a GPA of 3.91/4.00.My graduate research topic was a model based prediction that usedsimilar concepts in the estimation of stock prices. During my freetime, I used to read quantitative finance books, watch open courses,and develop trading algorithms. Furthermore, I am learningprogramming languages such as C++ and C. I believe that a combinationof new learned C++/C with my Python/MatLab background will strengthmy programming skills and prepare myself for any future challenges.

Inthe process of studying quantitative finance, I developed severaltrading algorithms using Python or MatLab. The first algorithm Ideveloped was a momentum strategy using optimized m-days and n-daysmoving average as trading signals. The strategy turned out to be toosensitive to parameters. The model would work for some time beforefailing with any signs. The parameters had to be tuned again,although getting the right time for tuning was tricky.

Ilater revised the momentum strategy by adding more techniqueindicators such as MACD, KDJ, EMV, and SAR, among others. I alsoadded a genetic algorithm to find the best combination of techniqueindicators. The model turned out to be more robust than simple movingaverage strategy. However, the genetic algorithm seems not to addvalue to the model since the ‘best technique indicatorscombination’ for one period does not work for another time. Afterstudying test results, I found out a 6-technique indicator that canrepresent the momentum of the stock. I also realized that theappropriate to trade stocks was when more than half of the indicatorsgave trading signals. The strategy works well in bull and bearmarket, although it fails in a range-bound market, as there is noclear trend. Sometimes, it gives many fake trading signals, whichleads to loss of money.

Additionally,I learned co-integration and pairs trading strategy from Quantopian’slectures and developed a version that adapts to the Chinese stockmarket. Since China’s market does not lack stocks, I used astrategy whereby, I purchased the undervalued stocks for speculationover a period. The returns depend on the quality stock pairs’co-integration relationship and trend of the market. As such, insteadof using the strategy alone, I am trying to develop a ranking systembased on co-integration for combination with other approaches.

Moreover,I familiarized with the part of quantitative finance research thatfocuses on forecast stock prices using models such as ARCH, GARCH,and SVM. In his book, Support Vector Machines Algorithms andFinancial Applications, Xun Liang applied SVM in foreign exchangemarket. The author claimed that the model has advantages such as lesstraining samples and better forecast accuracy outside trainingsamples. I used SVM on stock price forecast and found that it couldpredict stock prices accurately to some extent. However, the SVMprediction results cannot be used to trade directly because of randomnoise in stock prices. Currently, I am trying to apply SVM on rankingstocks based on their expected returns (Hu, Zhu, &amp Tse, 2013).

Comparedto general finance degree, the mathematical finance qualificationfocuses more on financial markets and theories behind the pricemovement. It is also a field I have great interest and passion. Thecurriculum provides one with the exposure to statistics formathematical finance, stochastic methods, computational techniques,trading algorithm, and portfolio theory, among others. The courseswill prepare me appropriately to work in the fields such asQuantitative Analysis or Strategic Analysis &amp Modeling.Therefore, I choose mathematical finance program (Ross, 2011).

Besidesgetting information from the admission office and program website, Iread reviews written by current and graduated students. I learnedthat the curriculum is designed to prepare students to complete tasksin the real world. The summer internship and career services helpstudents to gain full-time jobs after graduation. I also talked withalumni where I learned that university maintained good alumni networkand excellent relationship with companies. The resources are valuabletowards enhancing long-term career development. The institution hassome of the best professors in the world. I consider that thetheoretical knowledge will supplement the skills and experienceacquired over time.


Hu,Z., Zhu, J., &amp Tse, K. (2013, November). Stocks market predictionusing support vector machine. In 20136th International Conference on Information Management, InnovationManagement and Industrial Engineering(Vol. 2, pp. 115-118). IEEE.

Ross,S. M. (2011). Anelementary introduction to mathematical finance.Cambridge University Press.