Hoperation Fund
Quantitative Trading Lecture Series
Beginning in 2026, we launch a quantitative trading lecture series focused on AI-based models for financial data analysis. The lectures are hosted by Olivia and our advisors. If you are interested in joining, please contact the Hoperation Fund team by email.
Below is the lecture list.
Large Language Models for Stock Price Forecasting: A Hedge Fund Perspective
Abstract: This talk will first provide an overview of the latest applications of large language models (LLMs) in stock price forecasting. While many review papers discuss the broad applications of LLMs in finance, they often cover an overly wide scope. This presentation narrows the focus to a more specific area: the use of LLMs for stock price prediction. Drawing on research published between 2022 and early 2025, the talk categorizes existing applications into five major areas: sentiment extraction for predicting market movements, analysis of financial reports and earnings call transcripts, identification of cross-equity relationships, tokenization of stock price data for next-token prediction, and the development of agentic AI systems. The second part of the talk will discuss key challenges in applying LLMs to stock forecasting. These include issues such as the reliability of sentiment analysis, data leakage, database limitations, performance evaluation, illiquidity, and the fundamental challenge of market predictability. Finally, the talk will explore several promising directions for future research and practical improvements in the application of LLMs to stock price forecasting.
Speaker: Ms. Olivia Zhang
Time: 7:00 - 8:00 PM CT, August 2, 2026
Portfolio Management via Empirical Asset Pricing
Abstract: Following the last talk, this talk will further explore AI-based empirical asset pricing and examine how these methods can be applied to Hoperation Fund’s current investment portfolio. The discussion will connect theoretical models with practical portfolio-management decisions, including stock selection, signal construction, model evaluation, risk control, and implementation. Particular attention will be given to real-world challenges, deployment considerations, current limitations, and promising directions for future research and development.
Speaker: Ms. Olivia Zhang
Time: 8:00 - 9:00 PM CT, July 20, 2026
Roundtable Discussion on Stock Return Prediction with AI Using Fundamental, Technical, and Alternative Data
Abstract: This roundtable discussion will explore the use of AI models for stock return prediction by combining fundamental analysis, technical analysis, and alternative data. A key focus will be on how semantic information generated by large language models (LLMs) can impact and potentially improve the performance of these AI-driven prediction models.
Speaker: Ms. Olivia Zhang
Time: 10:00-11:00AM CT, July 8, 2026
Empirical Asset Pricing Using Machine Learning Models
Abstract: This talk is based on the paper Empirical Asset Pricing via Machine Learning by Gu, Kelly, and Xiu. It will introduce how machine learning can be used to predict stock returns, rank stocks, and construct long-short portfolios. The talk will also summarize why nonlinear models such as tree-based methods and neural networks improve upon traditional linear models in empirical asset pricing.
Speaker: Ms. Olivia Zhang
Time: 9:00-10:00AM CT, June 20, 2026
Time Series Forecasting Using Foundation Models
Abstract: The talk will cover topics ranging from statistical models to foundation models in forecasting, including zero-shot prediction, TimeGPT, challenges in working with real-world time series data, and explainability in deep learning models. Finally, the speakers will share insights on the future of AI-driven forecasting in financial markets.
Speakers: Ms. Olivia Zhang, Dr. Zhilin Zhang
Time: 7:00-8:00PM CT, June 13, 2026