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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 am a financial economist working in household finance. I study how people’s experiences and memories shape the financial decisions they make today, and how earnings opportunities and institutions influence their saving, investing, and trading.

Curriculum Vitæ [updated September 2026]

Research

My research connects households’ financial choices to their past experiences, the risks and opportunities they face, and the rules governing their decisions. I combine household panels and transaction records with models of memory and market exchange, paying close attention to what observed choices and survey responses can tell us. My interest in China’s financial development and policy also motivates work on housing markets, school rights, and the legacy of industrialization.

01

Experience and memory

How do past outcomes shape current financial choices? I study how memories of gains and losses guide investors’ selling decisions, and how labor-market conditions at entry relate to households’ later saving priorities. These projects connect personal histories to both investment behavior and the purposes households attach to saving.

02

Earnings risk and preference measurement

Which features of earnings prospects and stated preferences help explain financial choices? My job market paper separates upside earnings opportunities from downside risk in portfolio entry. Related work asks what repeated preference questions add to predictions of household wealth, and how the answer depends on response coding and information already observed.

03

Institutions and household adjustment

How do institutional rules change the costs of working, moving, and trading? I study earnings adjustment around social insurance mandates, retirement cash-outs after job endings, and housing exchange when a sale changes school rights. My China research also examines how valuation information relates to housing liquidity and how industrial development relates to household formation.

→ household finance, policy, and market exchange

// evidence — household surveys, transaction histories, and institutional changes in 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  C18 · D14 · G11 · J31
JMP

Beyond Variance: Asymmetric Labor Income Risk and Portfolio Entry

At a given earnings variance, a more upside-oriented historical earnings distribution predicts greater subsequent portfolio entry; earnings-progression controls reduce the association, and inference depends on estimated-risk uncertainty.
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
Tail composition and uncertainty about portfolio entry.
Tail composition and uncertainty about portfolio entry.
Reliability of assigned earnings-risk measures across constructions.
Reliability of assigned earnings-risk measures across constructions.
[02]
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.
[03]
JEL  D14 · E21 · E24 · J12 · J31
DRAFT

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

Data: Panel Data Research Center (PDRC) at Keio University, Project ID: 10446
Weaker entry conditions predict 4,750 yen more September saving, about 4,110 yen outside spouse-specific categories; regional and education-specific comparisons do not independently corroborate the national-cohort pattern.
Data: Japanese Panel Survey of Consumers (JPSC), Panel Data Research Center at Keio University
Show Figures
Entry conditions and the non-spouse–spouse saving contrast.
Entry conditions and the non-spouse–spouse saving contrast.
[04]
JEL  D14 · D15 · D91 · G11 · G51
DRAFT

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

Repeated answers add predictive information when baseline wealth is unavailable, but almost none when it is observed; the wealth gradient also depends on how response categories are represented.
Data: Panel Data Research Center (PDRC) at Keio University, Project ID: 10446
Show Figures
The predictive gain from repeated responses depends on whether baseline wealth is observed.
The predictive gain from repeated responses depends on whether baseline wealth is observed.
Response categories and future wealth rank; displayed-rate linearity is restrictive.
Response categories and future wealth rank; displayed-rate linearity is restrictive.
[05]
JEL  H55 · J22 · J32 · J38
WORKING PAPERw/1

Social Insurance Mandates and Earnings Reallocation: Evidence from Japan

with Yi Yao (USI)
Data: Panel Data Research Center (PDRC) at Keio University, Project ID: 9763
Earnings adjustments around Japan’s insurance expansion differ by households’ prior contribution arrangements; coverage among current workers does not describe coverage in the initially exposed cohort.
Show Figures
Earnings-band adjustment between announcement and enforcement; comparison-group sensitivity limits causal interpretation.
Earnings-band adjustment between announcement and enforcement; comparison-group sensitivity limits causal interpretation.
[06]
JEL  J12 · J23 · N35 · O25 · P23
DRAFTw/1

Industrial Composition and Marriage Timing in China’s Third Front

with Yi Yao (USI)
Heavy-industry exposure is associated with greater young singlehood inside Third Front provinces, but cohort comparisons do not establish a causal effect of the construction campaign.
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.
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.
JEL  G12 · G14 · R21 · R31
WORK IN PROGRESS

When Price Stops Clearing: Valuation Disagreement and Housing Illiquidity

Similar price-and-duration fits imply very different benefits from a 25% increase in valuation-signal precision: selling time falls 9.07% in the information-only model but barely changes in the combined model.
Presented at: Rising Scholar Conference in Finance (University of Zurich, 2026), UEA Summer School (LSE, 2026), USI IdEP Brown Bag (USI, 2024)
Show Figures
Selling time and the ask–sale gap on each side of the benchmark.
Selling time and the ask–sale gap on each side of the benchmark.
Model fit and information counterfactuals under flexible quote errors.
Model fit and information counterfactuals under flexible quote errors.
JEL  R21 · R31 · I28 · D83
WORK IN PROGRESS

Grandfathering at Sale: Nonportable School Rights and Housing Exchange

After a sale resets school rights, relative sales growth is 32–45% lower and transaction prices 4–5% lower in formerly key-school neighborhoods; lower prices need not restore trade.
Show Figures
A transfer resets school rights while eligible owners retain previous admission rules.
A transfer resets school rights while eligible owners retain previous admission rules.
Completed exchange in the historical assignment sample.
Completed exchange in the historical assignment sample.
JEL  D14 · G51 · J32 · J63
WORK IN PROGRESS

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

Retirement cash-outs are elevated after both voluntary and involuntary job endings; adjusted gaps versus continuers are 1.95 and 2.78 percentage points, with an imprecise difference between separators.
Show Figures
Retirement-account access and non-rollover around voluntary and involuntary job endings.
Retirement-account access and non-rollover around voluntary and involuntary job endings.
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