In Young Cho, Co-founder and CEO of DeepMind, discusses her unexpected transition from medicine to finance through a trading internship. She delves into the integration of trading, research, and software engineering. The conversation highlights the challenges of machine learning in chaotic environments and the evolution from basic models to deep neural networks. Cho also addresses the importance of robust data practices and explores the complexities of using Python notebooks in financial research.
In Young Cho's transition from aspiring doctor to quantitative trader highlights the importance of exploring one's true interests amid uncertainty.
The role of a quantitative trader combines multifaceted skills in programming, communication, and adaptability within a dynamic financial environment.
Machine learning in finance faces unique challenges from noisy data and rapid market changes, necessitating robust and flexible research frameworks.
Deep dives
Inyoung Cho's Path to Jane Street
Inyoung Cho's journey to Jane Street reflects a common experience among researchers and traders, characterized by exploration and uncertainty. Initially aiming for a career in medicine, Cho quickly realized her true interests lay in quantitative finance, leading her to seek opportunities in this field. After a challenging interview at Jane Street that left her intellectually stimulated yet exhausted, she decided to pursue an internship, which ultimately solidified her decision to work within finance. This dynamic environment, filled with new learning opportunities and supportive colleagues, has been a driving force in her career satisfaction.
The Evolving Role of a Trader
Cho describes her role as a quantitative trader as multifaceted and evolving, far beyond mere data analysis or program execution. Early on, she engaged in learning programming languages and important financial concepts, which included handling broker calls—a daunting task that included initial mishaps and miscommunications. The importance of interpersonal skills, such as effective communication with brokers, highlights the blend of technology and human interaction present within financial markets. This role demands adaptability as traders navigate both real-time decision-making and technological systems.
Research Methodology in Trading
Conducting research in trading involves several stages, including exploration, data collection, analysis, and productionization of models. Tools like Bloomberg terminals are essential for gathering data on trading patterns and market behaviors, while software frameworks facilitate running simulations and analyzing performance. Cho emphasizes the importance of understanding the nuances of data—ensuring its quality and structuring it correctly for analysis. This comprehensive approach enables researchers to formulate hypotheses that can significantly improve trading strategies and outcomes.
Challenges of Machine Learning in Finance
Machine learning in finance presents unique challenges due to the noisy nature of financial data and the need for real-time decision-making. The complexity of applying traditional machine learning practices becomes evident as financial events often disrupt expected patterns, requiring algorithms to adapt quickly to new information. Cho underlines the significance of interdisciplinary knowledge, where insights gained from models in other fields can inform financial models. As research frameworks advance, the efficiency and robustness of machine learning approaches will be crucial for capturing actionable signals in an increasingly dynamic market.
Future Directions in Trading Research
Looking ahead, Cho expresses excitement about the advancements in machine learning capabilities at Jane Street, particularly in the context of leveraging vast volumes of market data. The organization aims to improve its prediction accuracy through sophisticated models while exploring the integration of diverse data types, including images and texts. Challenges like transfer learning and using large-scale models are at the forefront of their research agenda, with a focus on maintaining a competitive edge in trading strategies. This evolution in research capabilities will enhance Jane Street's position as a significant player in finance, adapting to developments in technology and market needs.
In Young Cho thought she was going to be a doctor but fell into a trading internship at Jane Street. Now she helps lead the research group’s efforts in machine learning. In this episode, In Young and Ron touch on the porous boundaries between trading, research, and software engineering, which require different sensibilities but are often blended in a single person. They discuss the tension between flexible research tools and robust production systems; the challenges of ML in a low-data, high-noise environment subject to frequent regime changes; and the shift from simple linear models to deep neural networks.
You can find the transcript for this episode on our website.
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