Introduction

Institutional Investors: Theory and Evidence

Author
Affiliation

Calvin J. Chiou (邱健嘉)

Department of Finance · National Chengchi University

Published

September 10, 2026

Course Overview

At a Glance

Logistics

Code TBA
Day / Time Thursday 16:10–18:00
Credits 2
Weeks 16
Office Commerce Building 261233
Email jjchiou@nccu.edu.tw

What this course is about

  • Role of institutional investors in financial markets
  • Mutual funds · Pension funds · Insurance companies
  • Fund performance, agency problems, governance
  • ESG investing and long-term stewardship
  • Empirical methods applied to real data in R

What Makes This Course Distinctive

  • Combines theory with hands-on empirical work
  • Five R lab sessions integrated into the seminar
  • Labs use CRSP Mutual Fund data and Kenneth French’s public factor library
  • Lab notes distributed as runnable Quarto .qmd files — you can reproduce everything on your own machine
  • Readings drawn from JF, JFE, RFS, MS, JFQA — the core empirical finance journals

Learning Objectives

By the End of This Course, You Will Be Able To…

  1. Identify the major types of institutional investors and their structural incentives
  2. Analyze how institutional investors affect corporate governance, firm behavior, and market prices
  3. Critically evaluate empirical research on investor strategies, performance, and market impact
  4. Recognize identification challenges in this literature and how leading papers address them
  5. Implement core fund measures in R — factor alphas, DGTW benchmarks, active share, churn ratio, window dressing
  6. Develop and present an original research idea or literature review in this domain

Course Format

Three Modes of Instruction

📖 Lecture & Discussion

~50% of class time

  • Instructor introduces the week’s topic
  • Key papers reviewed and contextualized
  • Open research questions framed

🎤 Student Presentations

~30% of class time

  • Students present assigned or self-selected papers
  • 20–25 min presentation + 10 min Q&A
  • Two presentations per student

💻 R Labs

~20% of class time

  • Five instructor-led sessions
  • Implement measures from the readings
  • Runnable .qmd notes provided

Software and Data

R Stack

# Install before Week 2
pkgs <- c(
  "tidyverse", "tidyquant",
  "frenchdata", "broom",
  "gt", "scales", "slider",
  "fixest"
)
install.packages(pkgs)

Installation guide on Moodle.

Data Sources

Labs Data
1–3 Public (no login)
4–5 CRSP MF Holdings
  • Kenneth French’s factor library
  • ETF prices via tidyquant
  • CRSP Mutual Fund Holdings distributed by instructor

Assessment

Grading

Component Weight
Class Participation 20%
Paper Presentations 30%
Final Written Report 20%
Final Presentation 30%

Paper Presentations

  • Two presentations per student
  • Summarize question, data, method, findings
  • Situate within course literature
  • Raise ≥ 2 discussion questions
  • Slides required (PDF or PPTX)

Final Report Options

  • Literature review & synthesis (≥ 10 papers)
  • Research proposal with identification strategy
  • Replication & extension of an existing paper

Key Deadlines

Milestone When
Presentation slots assigned Week 1
R setup confirmed Before Week 2
Topic proposal (1 paragraph, via Moodle) End of Week 8
Final in-class presentation Weeks 15–16
Final written report (≤ 10 pages) End of Week 16

Course Schedule

Schedule Overview

Week Date Topic Type
1 Sep 10 Course Overview and Motivation Lecture
2 Sep 17 Types of Institutional Investors Lecture
3 Sep 24 Fund Performance I: Carhart 4-Factor Lab 1
4 Oct 1 Luck vs. Skill Debate Lecture
5 Oct 8 Fund Performance II: DGTW Lab 2
6 Oct 15 Benchmarking, Style Drift, Active Share Lecture
7 Oct 22 Tournament Behavior and Fund Flows Lecture
8 Oct 29 Manager Incentives and Fund Family Conflicts Lecture

Schedule Overview (cont.)

Week Date Topic Type
9 Nov 5 Fund Performance III: Bootstrap Inference Lab 3
10 Nov 12 Fund Holdings and Trading Behavior Lecture
11 Nov 19 Holdings Measures: Active Share, Churn, Window Dressing Lab 4
12 Nov 26 Career Concerns and Behavioral Biases Lecture
13 Dec 3 Institutional Monitoring, Voting, Engagement Lecture
14 Dec 10 Institutional and Common Ownership Measures Lab 5
15 Dec 17 ESG Investing and Long-Term Stewardship Lecture
16 Dec 24 Final Presentations Presentations

Weeks 1–2: Setting the Stage

Week 1 — Course Overview and Motivation

  • Why do institutional investors matter?
  • Bebchuk, Cohen & Hirst (2017, JEP) — agency problems of institutional investors
  • Presentation slots assigned; R setup check

Week 2 — Types of Institutional Investors

  • Mutual funds, pension funds, insurance companies, sovereign wealth funds
  • Asset concentration trends in the US and internationally
  • Taiwan and Asian institutional investor landscape
  • Gompers & Metrick (2001, QJE); Ferreira & Matos (2008, JFE)

Weeks 3–5: Measuring Fund Performance

Week 3 · [Lab 1] — Carhart Four-Factor Model

Download factor data → compute excess returns → estimate CAPM / FF3 / Carhart 4-factor in R

Week 4 — Luck vs. Skill Debate

  • Do mutual fund managers have genuine skill?
  • Fama & French (2010, JF); Berk & van Binsbergen (2015, JFE)

Week 5 · [Lab 2] — DGTW Characteristic-Adjusted Returns

  • Characteristic Selectivity (CS) and Characteristic Timing (CT) components
  • Fund flow, turnover ratio, active share approximation in R
  • Daniel, Grinblatt, Titman & Wermers (1997, JF); Wermers (2000, JF)

Weeks 6–8: Active Management and Agency

Week 6 — Benchmarking, Style Drift, and Active Share

  • Is your fund manager actually active?
  • Cremers & Petajisto (2009, RFS); Bollen & Busse (2005, RFS)

Week 7 — Tournament Behavior, Risk Shifting, and Fund Flows

  • Mid-year risk shifting; flow-performance relationship
  • Brown, Harlow & Starks (1996, JF); Sirri & Tufano (1998, JF)

Week 8 — Manager Incentives and Fund Family Conflicts

  • Cross-fund subsidization; career concerns
  • Gaspar, Massa & Matos (2006, JF); Bhattacharya, Lee & Pool (2013, JF)
  • ⚠ Topic proposals due end of Week 8

Weeks 9–11: Bootstrap, Holdings, and Trading

Week 9 · [Lab 3] — Bootstrap Inference for Luck vs. Skill

  • Joint block bootstrap resampling
  • Actual vs. simulated alpha distribution across funds
  • Replicates Fama & French (2010, JF, Section III)

Week 10 — TBA (lectured by Dr. Jarvinia Hsieh)

  • I will attend a conference in Japan.
  • Jar is expected to present a research topic regarding institutional investors.
  • Online (via Zoom or Google Meet).
  • Everyone should be present and be surveyed afterwards.

Week 11 · [Lab 4] — Active Share, Churn Ratio, Window Dressing

  • Three holdings-based measures from CRSP Mutual Fund Holdings
  • Cremers & Petajisto (2009); Agarwal, Gay & Ling (2014, RFS)

Weeks 12–14: Governance and Ownership

Week 12 — Career Concerns and Behavioral Biases

  • Employment risk and manager risk-taking
  • Kempf, Ruenzi & Thiele (2009, JFE); Frazzini & Lamont (2008, JFE)

Week 13 — Institutional Monitoring, Voting, and Engagement

  • Which institutions are active monitors?
  • How do mutual funds vote? Are they active voters?
  • Gillan & Starks (2000, JFE); Iliev & Lowry (2015, RFS)

Week 14 · [Lab 5] — Institutional and Common Ownership Measures

  • IO concentration (HHI), MHHI delta (Azar et al. 2018)
  • Panel regressions with fixest::feols()
  • Appel et al. (2016, JFE); Azar, Schmalz & Tecu (2018, JF)

Weeks 15–16: ESG and Wrap-Up

Week 15 — ESG Investing, Climate Risk, and Long-Term Stewardship

  • Do institutional investors drive CSR?
  • Climate risk as a financial risk
  • Chen, Dong & Lin (2020, JFE); Starks, Venkat & Zhu (2025, JF)

Week 16 — Final Presentations

  • All students present their final reports
  • Written reports due by end of week

R Lab Sessions

Five Labs, Tightly Linked to Theory

Lab Week What You’ll Build
Lab 1 3 Carhart 4-factor alpha for a set of ETFs
Lab 2 5 DGTW CS and CT components; fund-level flow and turnover
Lab 3 9 Bootstrap alpha distribution (Fama-French 2010 replication)
Lab 4 11 Active Share, Churn Ratio, Window Dressing Index from CRSP Holdings
Lab 5 14 Institutional ownership HHI; MHHI common ownership; feols() panel regressions

Each lab comes with a self-contained .qmd note distributed on Moodle before the session.

Lab 4 in Detail: Holdings-Based Measures

Three measures, one data source: CRSP Mutual Fund Holdings

  • Active Share (Cremers & Petajisto 2009)
    Fraction of the portfolio that differs from a benchmark index

  • Churn Ratio (Gaspar, Massa & Matos 2005)
    Holdings-based turnover capturing short-term trading intensity

  • Window Dressing Index (Agarwal, Gay & Ling 2014)
    Quarter-end inflation of winners + dumping of recent losers in disclosed holdings

. . .

Key practical step: merging holdings on wfundno / permno linkage table

Ready to Start?

Before Week 2 — Checklist

. . .

Full syllabus and reading list on Moodle.
Questions? jjchiou@nccu.edu.tw



Institutional Investors: Theory and Evidence

Calvin J. Chiou (邱健嘉)
Department of Finance · National Chengchi University
Fall AY114


See you next Thursday.

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