SYS:LIVE USR:yeungtailo@gmail.com BUILD 2026.08.r19 CPU --% MEM -.-GB ON_MARKET LUGANO --:--:-- CET
Tai Lo Yeung Tai Lo Yeung

Iam a PhD candidate in the Department of Economics at USI. I received my bachelor’s degree in financial engineering from 武漢大學 (WHU) in 2014 and a research master’s in finance from 中國社會科學院研究生院 (GSCASS) in 2019, where I studied at the Institute of Finance and Banking. That training sparked my lasting interest in China’s financial development and economic policy. I then earned a master’s degree in economics from 香港科大商學院 (HKUST Business School) in 2020 and another master’s degree in economics and finance from Barcelona School of Economics (UPF) in 2021 with a full tuition waiver. I was a visiting scholar in the Finance Department at the Wharton School (UPenn), from January to June 2025, hosted by Prof. Sylvain Catherine.

I study how lived experience becomes memory—and how that memory changes the financial decisions households make today. I also study how policy and institutions, particularly in China, shape household and market outcomes.

Curriculum Vitæ [updated August 2026]

Research

I am a financial economist working at the intersection of household finance, behavioral finance, and the Chinese economy. My research asks how experience and memory shape current decisions, and how financial development, policy, and institutions alter the opportunities and constraints households and markets face.

01

Experience becomes memory

Past gains and losses, recessions, and market crashes do not enter every decision mechanically. I study which experiences people retrieve, how they connect them, and how long those memories remain economically active.

02

Memory shapes beliefs

Experience matters through internal states we rarely observe directly. I examine subjective earnings risk and repeated preference elicitations, asking what these measures capture and what changes when measurement is noisy.

03

Households decide within institutions

I trace these forces into portfolios, trading, saving, retirement, and housing, and study how policy changes the choices available. My work on China uses policy reforms and historical industrialization to understand financial development, market exchange, and household formation.

→ portfolios, trading, saving, retirement, housing, and policy

// evidence — administrative records, transaction histories, policy variation, and long household panels from the U.S., China, and Japan

Fields of Interest

Primary

  • Household Finance
  • Behavioral Finance
  • Asset Pricing

Secondary

  • Chinese Economy
  • Urban Economics
  • Labor Economics

References

  1. Lorenz Kueng Institute of Economics (IdEP), USI · Swiss Finance Institute lorenz.kueng@usi.ch
  2. Jessica Wachter Department of Finance, The Wharton School · University of Pennsylvania jwachter@wharton.upenn.edu
  3. Alberto Plazzi Institute of Finance (IFin), USI · Swiss Finance Institute alberto.plazzi@usi.ch

News & Forthcoming

§ Video et Taceo §

Household finance · experience · memory · Chinese economy

Research

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[01]
JEL  R21 · R31 · I28 · D83
WORKING PAPER

Grandfathering at Sale: Nonportable School Rights and Housing Exchange

When a sale resets a grandfathered school right, completed housing flow falls sharply relative to ordinary communities, while public active-stock and asking-price comparisons move less.
Forthcoming: Rising Scholar Conference in Finance (University of Zurich, Sep 2026, poster), Real Estate and Urban Economics Conference (USI, Sep 2026, poster)
Presented at: UEA Summer School (LSE, 2026), USI IdEP Brown Bag (USI, 2024)
Show Figures
A Beijing school-assignment policy preserves pre-2019 single-school rights while a sale moves the buyer to multi-school assignment
The policy changes the school entitlement when the home is sold, not the physical dwelling.
Completed transactions stay near three per thousand units in formerly key-school communities while ordinary communities rise toward six; the exposed-relative PPML ratio is below one
Completed flow diverges after implementation; public active-stock and asking-price comparisons move much less.
[02]
JEL  C18 · D14 · G11 · J31
WORKING PAPER

Beyond Variance: Asymmetric Labor Income Risk and Portfolio Entry

Assigned lower and upper earnings tails have opposite conditional associations with later positive reported holdings; total span is imprecise, and excluding self-employed destination records sharply attenuates the lower-tail estimate.
Presented at: Fourth Georgia Tech – Atlanta Fed Household Finance Conference (Georgia Tech, 2026), AFA 2026 (Philadelphia, 2026), 19th International Behavioural Finance Conference (Booth School of Business, 2025), 14th International Moscow Finance Conference (HSE Moscow, 2025), SFI PhD Student Workshop (University of Zurich, 2025), 10th Luxembourg Workshop on Household Finance and Consumption (Central Bank of Luxembourg, 2025), German Finance Association (DGF) Annual Meeting 2025 Doctoral Workshop (University of Hagen, 2025), Gerzensee Alumni Conference (Study Center Gerzensee, 2024, 2025), Macro Finance Research Program (MFR) 2024 Summer Session for Young Scholars (University of Chicago, 2024), Frankfurt Summer School 2024 (The Deutsche Bundesbank, 2024), 2nd Workshop on Applied Macroeconomics and Monetary Policy (University of St. Gallen, 2024), RES PhD Conference 2024 (University of Portsmouth, 2024), USI IdEP Brown Bag (USI, 2023, 2024, 2025)
Show Figures
Coefficient plots show a wider lower earnings tail predicts less entry into stock and mutual-fund holdings, while a wider upper tail predicts more entry; total span is imprecise
Lower and upper tail distances point in opposite directions; an exact rotation leaves total span imprecise and signed composition negative.
Zero-inclusive tail-measure reliability is 0.74 in CPS and 0.60 in SIPP, but falls under positive-only transformations and after removing lower moments and demographics
Tail-measure reliability depends on population support and falls sharply under positive-only transformations and lower-moment adjustments.
[03]
JEL  D14 · D91 · G11 · G14 · G41
WORKING PAPERw/4

Navigating Through Fear and Greed: The Experience-Driven Disposition Effect

Past losses make investors quicker to sell winners, past gains slower. The channel is memory retrieval, not a change in preferences.
with Rong Liu (TJU), Jessica Wachter (Wharton), Michael Kahana (UPenn) and Yongjie Zhang (TJU)
Presented at: Finance Brownbag (University of Bristol, 2026), Workshop on Cognitive Economics (University of Zurich, 2026), SFS Cavalcade North America (University of Virginia, 2026), Memory, Beliefs, and Choice (MBC) (University of Pennsylvania, 2025), USI IdEP Brown Bag (USI, 2024)
Show Figures
Gain-by-loss and gain-by-win interaction coefficients across 5% to 30% return thresholds, opposite in sign throughout
Accumulated losses and gains move the disposition effect in opposite directions.
[04]
JEL  D14 · D15 · D91 · G11 · G51
DRAFT

Twice-Told Preferences? Repeated Elicitation, Response Coding, and Future Household Wealth

Repeated responses make persistence and coding assumptions visible, but add virtually no out-of-sample prediction beyond the first category; the apparent wealth gradient is driven by the questionnaire’s 40% endpoint.
Data: Panel Data Research Center (PDRC) at Keio University, Project ID: 9763
Show Figures
Pre-2016 elicited-rate means and split-block ORIV estimates predict several 2022 to 2024 household-finance outcomes, with uncertainty intervals
The repeated-proxy estimate is steeper for future wealth, but the broader portfolio margins are imprecise.
The mapped required-return mean predicts lower future wealth rank, while order-preserving recodings produce weaker and mixed estimates
The apparent wealth gradient is not invariant to how the response categories are encoded.
[05]
JEL  D14 · G51 · J32 · J63
DRAFT

When Jobs End: Layoffs, Quits, and Retirement Cash-Outs

Retirement cash-outs rise around both quits and involuntary separations relative to continued work, while the involuntary–voluntary difference is imprecise; the comparison group is central to interpreting a layoff coefficient.
Show Figures
Adjusted non-rollover differences are positive for voluntary and involuntary separators relative to continuers, while the involuntary-versus-voluntary difference is imprecise
Both separator groups differ from continuers; the incremental involuntary–voluntary contrast remains imprecise.
[06]
JEL  J12 · J23 · N35 · O25 · P23
DRAFTw/1

Industrial Composition and Marriage Timing in China’s Third Front

with Yi Yao (USI)
Industrial composition is associated with a persistent marriage-age gradient across three censuses and retrospective cohort histories, but current residence and campaign comparisons do not identify a causal Third Front effect.
Presented at: GLO-Guangzhou-2026 (IESR, 2026), USI IdEP Brown Bag (USI, 2026)
Show Figure
The 1982 resident-stock marriage-age gradient is compared with pseudo-1982 gradients reconstructed from retrospective first-marriage histories in the 2000 census
The 1982 census stock and retrospective first-marriage histories deliver similar point estimates, but they describe differently selected populations.
[07]
JEL  D14 · E21 · E24 · J12 · J31
DRAFT

A Cold Start: Recession Entry and the Purposes of Household Saving

Moving from late-bubble to trough entry conditions predicts about 4,750 yen more monthly saving, roughly 87% designated for family-common, child, or other purposes, alongside fewer durable purchases and more discouraged borrowing.
Data: Japanese Panel Survey of Consumers (JPSC), Panel Data Research Center at Keio University
Show Figures
The share of women entering a regular first job and the national job-openings ratio both fall across Japan's 1993 to 2005 Employment Ice Age
Labor-market tightness at school leaving tracks entry into regular first employment.
Residualized non-spouse minus spouse saving declines with better entry labor-market conditions, with an exact weighted slope of minus 7.25 thousand yen
The exact partial regression places the response in non-spouse rather than spouse-designated saving.
JEL  G11 · G40 · G41 · G51 · D14 · D91
WORK IN PROGRESSw/1

Whose Loss Is It?

with Chaojie (Jay) Liu (Bristol)
Preliminary evidence compares the same investor across self-directed and robo-advised accounts, asking whether who chose the holdings changes how a gain or loss is acted upon.
i.

Python

Here is a highly recommended course to learn Python coding for economics:

ii.

MATLAB

  • Structural Estimation [in preparation]

A very good introductory lecture on structural estimation is available on YouTube, offered by Prof. Michael Keane.

iii.

R

  • Structural Estimation [in preparation]
Serkan Karadas and Minh Tam Tammy Schlosky, Determinants of Retirement Savings Decisions: Evidence from a 401(k) Plan
Chicago, IL —
Federico Baldi-Lanfranchi, Cost Savings in Mutual Funds
SFI PhD Student Workshop, Zurich —

Undergraduate

Essentials of Corporate Finance

Postgraduate

Empirical Methods in (Household) Finance

  • C0: TBC

Topics in Behavioral Finance

  • TBC

Structural Estimation

  • C0: This section introduces the simplest structural estimation framework from Blundell, Pistaferri, and Preston (2008), demonstrating how to apply the Generalized Method of Moments (GMM) to estimate parameters in a log-linear consumption model.
  • C1: TBC

Using Claude AI for Research

  • Slides: A hands-on workshop on using Claude AI for research and writing.

This page documents my investment journey beginning in 2025. I will track and report the realized returns for each investment.

Portfolio as of July 20, 2026

On June 18, 2026, I purchased four mutual funds focused on China’s AI supply chain, in response to the U.S. government’s block on Claude’s Fable 5 model on June 12, 2026. I realized the position on July 20, 2026, at a loss of approximately 15%.

Portfolio as of April 16, 2025

On February 5, 2025, I purchased the following stocks in response to the AI boom in China:

  • Alibaba – 28.1% allocation at HKD 97.00 per share; realized on February 13, 2025 at HKD 115.40.
  • HSBC – 46.4% allocation at HKD 80.15 per share; realized on March 11, 2025 at HKD 85.00.
  • Xiaomi (W) – 25.5% allocation at HKD 39.15 per share; realized on February 13, 2025 at HKD 41.80.
Tokyo, January 2017
TokyoJan 2017
Praha, December 2022
PrahaDec 2022
Lugano, December 2022
LuganoDec 2022
Zermatt, December 2023
ZermattDec 2023
NY, May 2025
New YorkMay 2025
UT, May 2025
UtahMay 2025