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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 the same earnings variance, more upside-oriented earnings predict greater market entry but lower subsequent risky shares among initial holders; the real-log estimates reveal portfolio responses that variance alone misses.
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
Upside composition predicts 0.88 percentage point more entry, 0.36 point higher risky shares among initial nonholders, and 1.62 points lower shares among initial holders.
More entry, lower shares among initial holders. Real-log earnings estimates hold total variance fixed. Intervals condition on estimated CPS moments and exclude first-stage remeasurement uncertainty; these are associations, not causal effects.
The minus 3.68-point risky-balance contribution and plus 2.06-point denominator contribution sum to the minus 1.62-point holder-share association.
The negative risky-balance contribution outweighs an offsetting denominator contribution. This accounting includes valuation changes and later exits, not just net trades; it is not a causal mechanism decomposition.
An illustrative equal-mean, equal-variance earnings rotation raises model entry from zero to 20 percent and lowers incumbent risky shares from 68.75 to 68 percent.
Same mean and variance, different portfolio choices. Participation costs and safe remaining human capital generate opposite responses in an illustrative model, not a calibration to the empirical estimates.
Entry estimates are positive across four earnings constructions; the negative holder-share estimate is precise for real-log earnings but imprecise for nominal-arc earnings.
Entry is positive across four earnings constructions; holder-share evidence is strongest under real-log earnings. This comparison does not establish a statistically significant difference between the real-log and nominal-arc slopes.
[02]
JEL  D14 · D91 · G11 · G14 · G41
WORKING PAPERw/4

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

Past realized losses are associated with a stronger disposition effect and gains with a weaker one; a memory-based recall model reproduces the asymmetry without changing 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: Labor-Market Entry and the Allocation of Household Saving

Data: Panel Data Research Center (PDRC) at Keio University, Project ID: 10446
Saving designations predict financial outcomes beyond total saving: wife-designated saving predicts growth in her securities holdings, while the evidence does not establish a causal effect of early-career adversity.
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 and 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
Dependent spouses show a larger decline in earnings-band membership than direct contributors around Japan’s insurance expansion, but attrition and imprecise transitions limit conclusions about avoidance versus covered employment.
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

More buyer encounters consistently speed sales and raise seller value across fitted models; better valuation information has less uniform benefits and can reduce the value of waiting for an optimistic offer.
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.
Conditional model comparisons: a 20% reduction in posterior uncertainty versus 25% more buyer encounters. Faster turnover need not raise seller value; these are not equal-cost policies or sampling confidence intervals.
Conditional model comparisons: a 20% reduction in posterior uncertainty versus 25% more buyer encounters. Faster turnover need not raise seller value; these are not equal-cost policies or sampling confidence intervals.
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 accompany voluntary job changes even without an employment interruption, and the voluntary-separation gap is larger among workers with limited prior financial assets.
Show Figures
Worker person-year shares and subsequent non-rollover rates by prior retirement balances and financial resources.
Worker person-year shares and subsequent non-rollover rates by prior retirement balances and financial resources. These descriptive rates are distinct from the adjusted separation contrasts.
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