Hi, welcome to my home page! I am Shiman Hu, a PhD candidate in Financial Technology from HKUST supervised by Prof. Yingying Li, Prof. Danyang Xie, and Prof. Xinghua Zheng. My research interests are High-frequency and High-dimensional Financial Econometrics, LLM-Enhanced Agent-Based Macroeconomics, and Machine Learning.
My story
The Yangtze River, a shimmering presence, flowed past my childhood home in Chongqing. I'd spend hours watching the boats and ships, each one a tiny world gliding towards an unknown horizon. This sparked a persistent question: Where were they going? What lay beyond the familiar curves of the river? That early curiosity, that yearning for a "bigger world," became a driving force, quietly shaping my aspirations. This nascent ambition was nurtured at Bashu School, a place that not only sharpened my thinking but also instilled in me a spirit of persistence and optimism. My parents, too, played a crucial role. My architect father, conjuring cities from his imagination, and my businesswoman mother, with her contagious energy, inspired me to be intelligent and powerful – to shape my own destiny, much like those ships charting their courses on the vast river.
This desire to understand and shape my own path led me to take the college entrance exam, and I was accepted into the Financial Engineering program at the Southwestern University of Finance and Economics (SWUFE). This marked a turning point; my early, somewhat undefined ambitions began to coalesce. At SWUFE, I discovered my passion for finance and economics research. This passion led me to actively participate in corporate finance research, culminating in the co-authorship of a paper with my supervisors during my undergraduate years. This early success solidified my commitment to a research-focused career.
The allure of the “bigger world” first sparked by those boats on the Yangtze continued to resonate deeply. Therefore, when the opportunity to participate in SWUFE's 2+2 program presented itself, I embraced it without hesitation. At 19, I moved to Baruch College in New York City to immerse myself in Financial Mathematics. It was a challenging, yet incredibly exciting, transition. At Baruch, I encountered the concept of "stochastic" – a term that might sound complex but describes something truly beautiful and fundamental to market behavior. I vividly remember the joy of using Python to generate my first Ornstein-Uhlenbeck process; it felt like uncovering a hidden language governing the seemingly chaotic movements of the market. This experience crystallized my research focus: I wanted to understand the financial market through the rigorous application of statistical models to complex datasets.
This refined focus, combined with my growing expertise, led me to the Hong Kong University of Science and Technology (HKUST). I was fortunate to be selected by Professor Yingying Li from a large and competitive group of applicants to join her research group, specializing in financial technology. Financial technology is a broad and rapidly evolving field, and my work within it is specifically concentrated on using statistical models, machine learning algorithms, and textual analysis techniques to analyze large financial datasets. My ongoing projects utilize high-frequency stock transaction data, high-dimensional data, and textual data (such as news article), all with the aim of gaining a deeper understanding of market behavior and underlying dynamics.