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Author: Jack Kincannon | Major: Data Science | Semester: Spring 2023
The decision to research autonomous portfolio management systems was not arbitrary; it was a natural progression of my academic interests and career goals. I developed a fascination for the intersection of data science and financial markets through Dr. Schubert, my mentor, and the authors, Michael Lewis, and Malcom Gladwell.
The application of data science techniques to finding signals in a world of noise was a captivating prospect. My summer studying finance at the London School of Economics and Political Science in 2021 further solidified this fascination. When it came time to choose a research topic, there was no doubt in my mind – I wanted to create an automated portfolio management system.
My journey, however, was not without its hurdles.
Setting out, I was eager to delve into high-frequency trading strategies. However, I soon discovered, that I would be held back by regulations from the U.S. Securities and Exchange Commission. These regulatory hurdles, compounded by my employment status with a wealth management firm, meant my research had to be flexible and innovative from the outset.
Choosing a reliable brokerage was another significant challenge. When my chosen broker, TD Ameritrade, was acquired by Charles Schwab, the transition led to the unavailability of their API. My plans for live trading were suddenly in disarray. Other alternatives had their unique challenges, and the quest for a suitable brokerage became a complex process requiring flexibility and a deep understanding of the landscape.
As the Spring 2023 semester commenced, time and data became my greatest adversaries. The transition of TD Ameritrade and the time constraint presented by the semester period meant that data acquisition and management became a formidable task. Back testing, too, came with its own hurdles due to the complexities of data structures and unclear documentation for popular libraries. This phase of my research taught me crucial lessons about problem-solving and perseverance.
The invaluable lessons I learned from my research project now pave the way for an exciting new chapter in my career. This August, I will be moving to Kansas City to join Arvest Wealth Management’s Portfolio Management & Research Team as a Data Strategy Analyst. I look forward to bringing all my acquired knowledge into practice in this new role.
This journey, albeit challenging, has been transformative. I expanded my knowledge on a range of topics, from MLOps and cloud computing systems to time-series, econometrics, forecasting, and more. My Python programming was the largest beneficiary, improving significantly since the start of the project.
Yet, the learning process was not just about the technical aspects. Late-night programming sessions and the occasional struggle to stay committed after a full day of work taught me the immense value of discipline. Keeping focused on my goals and staying true to my timelines became critical, further reaffirming the importance of discipline in achieving success.
As I look back, I appreciate every hurdle and victory in this journey. My experience has reinforced my passion for combining data science with finance and politics; further shaping my academic pursuits, and career aspirations. I eagerly anticipate the opportunity to apply my skills in the real world, continuing to navigate the fascinating landscape of financial technology.
I am extremely grateful for the resources and opportunities provided by the University of Arkansas’s Honors College in facilitating my undergraduate research efforts. My summer experience studying finance at the LSE, which was made possible through their support, deepened my understanding of the subject matter and solidified my interest in automated portfolio management systems. Without their assistance, my journey and research would not have been possible. Their commitment to fostering academic exploration, and providing resources for undergraduate students like me, has been invaluable.