Research
Research Interests
Asset Pricing, Behavioral Finance, Macro-Finance
Working Papers
Investors view cash in their savings accounts differently from cash recycling in their stock brokerage accounts. I propose a novel “temperature” framework for financial resources, in which the former is labeled “cold cash” and the latter “hot cash.” I find individual investors tend to buy stocks more cautiously when using cold cash than when using hot cash. Exploiting the quasi-natural experiment of the 2016 Chinese IPO lottery reform, I show the effect of cash temperature on investors' cautiousness in stock selection is causal. To explore the mechanism, I propose a portfolio choice model featuring preferences with temperature-dependent sensitivity to future gains and losses. The model generates the empirical patterns documented in this paper and provides a cash-temperature interpretation for understanding other puzzles in the literature.
with Allen Hu
We decompose the trading volume of individual stocks into extensive and intensive flows, where extensive flows are trading volumes of investor entry and exit while intensive flows are position adjustments by continuing holders. To distinguish their differential origins and impacts, we model extensive and intensive flows in a variant of Grossman-Stiglitz. The model features a group of constrained investors, who can hold only one of the two risky assets, and a group of unconstrained arbitrageurs, who can hold both assets. Constrained investors endogenously move between two assets, creating extensive flows in the market. After shocks to the payoffs of both assets, only continuing holders can observe the true signal, while others need to infer the signal from price changes. Since the extensive flows are unobservable, they are not accurately incorporated into signal inference, which leads to the Subjective Flows Equilibrium (SFE). Compared to the benchmark of Rational Expectation Equilibrium, SFE predicts differential effects of extensive and intensive flows on asset returns and volatility. We provide evidence to confirm these predictions using daily transaction records of a large group of individual investors.
This paper studies how investors evaluate themselves and its implications. In my model, investors form subjective beliefs about both the stock currently held in portfolio and their trading talent and update their beliefs through learning with fading memory. I calibrate the memory decay parameters to account-level trading records and show that beliefs updating for talent is about 7 times more sensitive to return signals than that for the stock in portfolio. Consequently, the model predicts that stock replacing typically happens after a good performance of the existing stock, providing a dual learning perspective on the disposition effect. This framework also accounts for the widely documented performance-contingent trading intensity and investor attrition, which cannot be reconciled with the decreasing-gain property of the standard Bayesian learning.