Norman Guo

3674 Lindell Blvd. · Saint Louis, MO 63108 · norman.guo@slu.edu

I will join Saint Louis University as an Assistant Professor in Finance this Fall. My research broadly focuses on FinTech, Machine Learning, Investment, and Corporate Finance, with a particular interest in uncovering hidden patterns on mutual funds, hedge funds, and financial analysts using explainable machine learning models.

Research Interest

  • FinTech: AI, Machine Learning, Deep Learning
  • Investments: Hedge Funds, Mutual Funds, Portfolio Managment
  • Corporate Finance: Financial Analysts

Download Norman's Curriculum Vitae


Research

Publication

Hedging Performance of Multiscale Hedge Ratios
with Jahangir Sultan, Antonios Alexandridis, and Mohammad Hasan, 2019, Journal of Futures Markets

In this study, we combine the wavelet multiscale model and neural network to improve the hedging performance of multiple classes of assets over traditional hedging models.

Working Paper

Decoding Mutual Fund Performance: Dynamic Return Patterns via Deep Learning [Job Market Paper] [Slide]

Presentations: Economics of Financial Technology Conference 2022 (scheduled), 2022 Atlanta Rising Scholar Symposium in Finance, MFA 2022, Georgia State University, Saint Louis University, Hofstra University

I employ a state-of-the-art sequential deep learning model to understand and predict dynamic patterns in mutual fund returns. The model predicts sequences of future returns and offers interpretable insights. A long-short portfolio based on the model’s prediction generates a 2.8% annualized Carhart 4-factor alpha, and this abnormal performance is persistent for up to four years. The model captures dynamic features of mutual fund strategies related to company fundamentals and macroeconomic states. Fund returns are most informative when they happen after earnings announcements for stocks held by the funds. Historical performance and macroeconomic variables are the most important determinants of future fund return patterns and performance.

Can Machines Understand Human Decisions? Dissecting Stock Forecasting Skill [Slide]

Semi-finalist of the FMA 2021 Best Paper in FinTech
Presentations: 2022 AsianFA Annual Conference (scheduled), Hawaii Accounting Research Conference 2022, MFA 2022, Renmin University, Baruch College, FMA 2021 , SFA 2021, Nankai University, Fudan University, University of Arizona, Georgia State University, University of Georgia, Iowa State University, University of Minnesota, Xiamen University, Huazhong University of Science and Technology, Beijing University, Atlanta PhD Consortium, Shanghai Jiaotong Univeristy, CHUK-Shenzhen

We use machine learning (ML) to provide a novel methodology to determine analysts' skills and effectively aggregate the forecasting opinions of analysts to form a crowd wisdom-based earnings forecast. Our machine-identified skilled analysts persistently outperform expert-picked star analysts. We find that machines rely on nonlinear interactions of analyst characteristics, such as past skill and efforts, to make predictions, unlike human experts, who lean more on relation-based information such as brokerage size.

The Impact of AI Adoption on Hedge Fund Performance [Slide]

Presentations: Global Finance Association 2021, FMA 2021 , SFA 2021

We examine the impact of AI adoption on hedge fund performance and investment strategies. We find that AI adoption improves hedge fund performance by 2.64% annually. AI adoption also reduces fund risk and increases shape ratio and information ratio. The adoption of AI is associated with a greater number of holdings in the portfolio and less concentration in the local stocks.

Why do actively managed mutual funds hold ETFs? Evidence on liquidity management

Presentations: FMA 2019, Georgia State University

I find that investment managers in actively managed mutual funds trade exchange-traded funds (ETFs) for liquidity management. The results show that funds that do not use index ETFs exhibit lower returns when they experience fund flow. The performance of funds that use index ETFs, however, is independent of investor’s liquidity demands.


Teaching

Blockchain and business disruption

Instructor, Georgia State University

This course provides an introduction to blockchain technology and its disruptive roles in business. Students will have hands-on and problem solving experiences that can be useful in blockchain applications and innovation.

  • Fundamentals of Blockchain Technology
  • Blockchain and Business Disruption
  • Cryptocurrencies
  • Ethereum and Programming
  • Blockchain Platforms
August 2021 - Present

Corporate Finance

Instructor, Georgia State University

Instucted under Flipped Classroom format with online reserouce of textbook, practices, and exams.

August 2019 - Present

CFA Exam

Curriculum Lead and Lecturer, Utoollearning

Design and teach the course of Quantitative Methods, Portfolio Management, and Equity Investments in the CFA Level I exam.

March 2015 - May 2016

Interests

In my personal time, I enjoy hiking with my family. I also enjoy running, cycling, and kayaking.

When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows. I am an aspiring chef, and I spend a lot of my free time exploring the latest technology advancements in Artificial Intelligence.