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 that individual investors tend to buy stocks more cautiously when using cold cash than when using hot cash. Exploiting the quasi-natural experiment of the Chinese IPO lottery reform in 2016, I show that the effect of cash temperature on investor's cautiousness in stock selection is causal. To explore the mechanism of this effect, I propose a portfolio choice model featuring preferences with temperature-dependent sensitivity to 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.
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 continuative holders. Empirically, extensive flows are dominant (as under-diversified investors move across individual stocks) and unobservable (as only aggregate trading volumes are disclosed to market participants). To distinguish their differential origins and effects, 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. Such an under-diversification leads to extensive flows of constrained investors as they endogenously move between the two assets. After a shock to one asset’s payoff, only its holders can observe the true signal, while others need to infer the signal from price changes. Since the extensive margin movement between two assets is not observed, it is not accurately incorporated into the signal inference, which leads to the Migration Neglected Equilibrium (MNE) where the magnitude of the signal is over- or under-estimated. Compared to the benchmark of Rational Expectation Equilibrium (REE), MNE predicts over- or under-reactions to shocks, excess trading volume and excess comovement between the two assets. We confirm these predictions using daily transaction records of a large group of individual investors in the Chinese stock market.
This paper studies how investors evaluate themselves and its implications for understanding the stylized facts documented for individual investors. In the 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. With memory decay parameters calibrated to individual trading records, I 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 experiencing good performance of the existing stock, which provides a dual learning perspective on the disposition effect. This framework also accounts for the well-known performance-contingent trading intensity and investor attrition, which cannot be reconciled with the decreasing-gain property of standard Bayesian learning.