Oxford-Man Institute of Quantitative Finance

OxfordOMI

Dr Hao Ding (丁昊)

Postdoctoral Research Fellow

Oxford-Man Institute of Quantitative Finance, University of Oxford

My research encompasses empirical asset pricing, generative and agentic AI, big (alternative) data in finance, and quantitative finance. I develop trading strategies across multiple asset classes using large language models to identify behavioral investment signals, and address systemic risk detection. I received my PhD in Finance and Econometrics from Warwick Business School, University of Warwick.

hao.ding@omi.ox.ac.uk LinkedIn GitHub Oxford Website

Research

Working Papers

Retail Investor Attention and Mutual Fund Performance: Evidence from EDGAR Log Files

2024

SSRN →

I develop a measure of retail investor attention to mutual funds, Total Views, by distinguishing between retail and sophisticated investors’ access to fund shareholder reports (N-CSR) via EDGAR. Total Views positively predicts retail fund flows and performance, with a 0.28% rise in future flows and a 0.02% improvement in alpha. An equal-weighted high-minus-low portfolio based on abnormal Total Views yields positive returns. Total Views strengthen the flow–performance relationship. Investor attention on reports from outperforming funds help attract additional inflows but do not cause more outflows for underperforming funds. Further analyses show that fund shareholder reports offer valuable, non-time-sensitive information throughout the year.

Retail Investor AttentionEDGARMutual FundShareholder Reports

Presentations: 2024 Financial Management Association (FMA) Annual Meeting, 2025 Swiss Society for Financial Market Research (SGF) Conference, 2025 Sydney Banking and Financial Stability Conference (SBFC), OMI workshop

Mutual Fund Strategy Changes and Performance

2023

SSRN →

I introduce a new active portfolio management measure, Strategy Shifting, which represents the divergence of the actual weights from the expected weights that a fund should assign to stocks if it follows previous stock characteristics based trading strategies. The measure assesses changes in trading strategies in response to shifts in fundamental information and is free from the benchmark mismatch problem. I show that mutual funds actively altering strategies contribute to improved fund performance. The finding remains robust after controlling for other active management measures and fund characteristics.

Mutual Fund PerformanceActive ManagementPortfolio Management

Presentations: 2024 Financial Management Association (FMA) European Conference, 2024 Financial Management Association (FMA) Doctoral Student Consortium, 16th Society for Financial Econometrics (SoFiE) Annual Meeting, 31st Spanish Finance Association (AEFIN) Finance Forum, Behavioural Finance Working Group 17th Annual Conference, 2024 Southwestern Finance Association (SWFA) Annual Meeting, Nippon Finance Association 32nd Annual Conference, 2023 World Finance & Banking Symposium, Warwick Business School Finance Group Brown Bag Seminar

Ongoing Work

The Solo-Authorship Reversal: Generative AI and Academic Team Formation

2026

Draft available upon request

Identifies two channels through which AI restructures academic collaboration using difference-in-differences on 1.4 million preprints.

Generative AIAgentic AIAcademic AuthorshipLabor SubstitutionTeam Science

Crash Risk and Market Resilience in Retail-Dominated Markets

2026

with Alvaro Cartea

Draft available upon request

Examines the effect of China’s short-selling ban on crash risk using a difference-in-discontinuities design in the Chinese A-share market.

Short-Selling ConstraintsCrash RiskChinese A-Share MarketRetail Investors

Asset-Based Lending as a Leading Indicator of Systemic Crises

2026

with April Goulding

Draft available upon request

Investigates ABL securitisation as an early warning signal for systemic crises using Temporal Fusion Transformers and Graph Attention Networks.

Asset-Based LendingSystemic RiskMachine LearningEarly Warning Systems

Attention Cycles

Forthcoming

with Alvaro Cartea, Zichuan Guo

SEC Rulemaking and Comments

Forthcoming

with Su Li, Danmo Lin

Beauty in Numbers, Cost in Trades

Forthcoming

with Shubo Kou, Xiyuan Ma

Earlier Work

Readability and Neutralness in Mutual Fund Shareholder Reports

2024

Draft available upon request

I use large language models (LLMs) fine-tuned on financial texts to assess the readability and neutrality of mutual fund shareholder reports. A neutral tone typically predicts increased fund inflows, but this effect lessens with higher readability. When reports are highly readable, neutrality leads to outflows for outperforming funds and doesn’t boost inflows for underperforming ones. Retail investors respond minimally to neutrality unless reports are highly readable, and their reactions are less pronounced than those of institutional investors. Limited access to shareholder reports restricts retail investors’ information acquisition. These findings support the adoption of the Tailored Shareholder Report Rule.

Mutual Fund PerformanceShareholder ReportsLarge Language ModelsTextual Analysis

Projects

AI Investment Platform

Full-stack · Live demo

Multi-model AI stock research platform with 3D portfolio optimisation, SEC filing analysis, cross-validated sentiment, and an AI personal banker chatbot.

Next.jsFastAPILLMThree.jsPostgreSQL

Strategy Backtester

Coming soon

Backtest common investment strategies (momentum, value, mean reversion) against historical market data with performance metrics and risk analytics.

BacktestingSharpe RatioDrawdownFactor Models

EDGAR Filing Analyser

Coming soon

Analyse SEC EDGAR filing access patterns to measure retail investor attention — the methodology behind my research on mutual fund performance.

SEC EDGARRetail AttentionN-CSRLog Files

Open Source Research

Coming soon

Open-source tools and datasets for financial research including EDGAR filing download utilities and textual analysis pipelines.

PythonNLPData PipelinesOpen Source

Curriculum Vitae

Postdoctoral Research Fellow

Oxford-Man Institute of Quantitative Finance, University of Oxford

2025 - Present

Researcher (Statistics)

National Education Union

2025 - Present

PhD in Finance and Econometrics

Warwick Business School, University of Warwick

2020 - 2025

MSc Risk Management and Financial Engineering (Distinction)

Imperial College London

2018 - 2019

Request Full CV

Teaching

Teaching Fellow

Queen Mary University of London, School of Business and Management

2022 - 2024

Python, R, Big Data & Generative AI Workshops

Weekly · UG/PG · Designed and facilitated weekly workshops

Quantitative Research Support

UG/PG · Research support for undergraduate and postgraduate students

Dissertation Training

UG/PG · One-on-one dissertation training for UG and PG

Introduction to Management Accounting

BUS140 · UG · Teaching Assistant

Senior Graduate Teaching Assistant

Warwick Business School, University of Warwick

2021 - 2024

Big Data Analytics4.86/5.00

IB9KW0 · PG · R lab sessions

Programming for Quantitative Finance4.67/5.00

IB9JH0 · UG · C++ lab sessions

Investment Management4.50/5.00

IB3570 · UG · Seminars + Excel/Python projects

Finance in Practice4.31/5.00

IB2D90 · UG · Quantitative finance seminars

Contact

Oxford Email

hao.ding@omi.ox.ac.uk

Personal Email

haodingresearch@gmail.com

hao-ding-oxford

GitHub

haodingresearch

More About Me

Snowboarder & Car Guy

I modified and tuned a supercharged, stripped & caged GT86 with 3D-printed and fabricated mods for track days.

Registered Marshal with Motorsport UK since 2022.