Working Paper
Asymmetric Labor Income Risk: Implications for Risk-Taking in Financial Markets
[LINK]
[Slides] (available soon)
Show Abstract
Abstract: This study investigates the asymmetric effects of labor income risk on asset allocation, contingent on the level of income growth skewness. Skewness acts as a reliable measure for assessing the performance of the labor market at a macro level. This is because negative macroeconomic (or industry-level) shocks, which are undiversifiable, result in a distribution of labor income changes that is skewed to the left, and the opposite occurs with positive shocks. Skewness of labor income growth is also important to understand the effect of income volatility -- the measure of background risk traditionally used in the literature -- on portfolio choice. Using microdata from the SIPP, I find that investors who experience high labor income volatility make different financial investment decisions depending on the skewness. I show that the observed positive correlation between labor income volatility and households' risky asset holdings, once linked to risk-tolerant individuals choosing high-risk jobs, is primarily influenced by the upper quantile of labor income distribution. This empirical finding aligns perfectly with theoretical expectations, and challenges the dominant empirical methods used to assess labor income risk. It suggests that the impact of labor income risk on asset allocation is not fully captured by considering only the second moment of labor income growth.
Presented at: USI IdEP Brown Bag (USI, 2023, 2024), 2nd Workshop on Applied Macroeconomics and Monetary Policy (University of St. Gallen, 2024), Frankfurt Summer School 2024 (The Deutsche Bundesbank, 2024), Macro Finance Research Program (MFR) 2024 Summer Session for Young Scholars (University of Chicago, 2024), Gerzensee Alumni Conference (Study Center Gerzensee, 2024), RES PhD Conference 2024 (University of Portsmouth, 2024)
Navigating Through Fear and Greed: The Experience-Driven Disposition Effect
with Rong Liu (TJU)
[Slides] (available soon)
Show Abstract
Abstract: Previous research has demonstrated that trading experience of individual investors can mitigate the behavioral bias known as the disposition effect, which is characterized by a greater tendency to realize gains rather than losses. This study investigates whether investors learn differently from past experiences based on gains versus losses, aligning with prior experimental evidence that suggests individuals are disproportionately sensitive to poor outcomes when shaping their investment beliefs. By analyzing transaction-level data from a large brokerage firm in China, we assess how this pessimism bias affects the degree of the disposition effect. Our findings indicate that after experiencing losses, investors are more inclined to realize gains and less likely to realize losses, thereby exhibiting a stronger disposition effect driven by regret aversion or a pessimistic market outlook. Overall, our research underscores the significant and asymmetric influence of investors’ experiences on the prevalence of behavioral biases.
Presented at: USI IdEP Brown Bag (USI, 2024), Memory, Beliefs, and Choice (University of Pennsylvania, 2025)
Consumption under Constraints: Uncovering Inequality in Discretionary Spending
Presented at: USI IdEP Brown Bag (USI, 2022)
Tax-Induced Labor Supply Distortions and Household Welfare: Evidence from Japan
慶應義塾大学パネルデータ設計・解析センター（PDRC）プロジェクトID：6721
Panel Data Research Center (PDRC) at Keio University Project ID: 6721
Presented at:
Work in Progress
Behind the Housing Market Boom: Sticky Listing Price and its Bargaining Power
Presented at: USI IdEP Brown Bag (USI, 2024)
I will share some code here for solving HA models.
Here is a highly recommended course to learn Python coding for economics: Household Behavior over the Life Cycle by Prof. Thomas H. Jørgensen.
A very good introductory lecture on structure estimation is available on YouTube, offered by Prof. Michael Keane.
I will share some notes for CFA/FRM/PRM.
Essentials of Corporate Finance
Math review
Investment Journey
This page documents my investment journey beginning in 2025. I will track and report the realized returns for each investment.
Portfolio as of March 20, 2025
On February 5, 2025, I purchased the following stocks in response to the AI boom in China:
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Alibaba
– 28.1% allocation at HKD 97.00 per share, I executed the return on February 13, 2025, at HKD 115.40 per share.
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HSBC
– 46.4% allocation at HKD 80.15 per share, I executed the return on March 11, 2025, at HKD 85.00 per share.
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Xiaomi (W)
– 25.5% allocation at HKD 39.15 per share, I executed the return on February 13, 2025, at HKD 41.80 per share.
