Gross trading volumes in financial markets are large and far exceed return volatility. In contrast, "net volume" - trading from persistent portfolio reallocations - is substantially lower, as it excludes transitory round-trip trades. This observation reveals a fundamental tension: If return volatility is high, while net volume is low, then market participants either agree with each other (they are "homogeneous"), or they are not sensitive to price changes, resulting in large price impacts for demand shocks (they are "inelastic"). We formalize this tradeoff and demonstrate that the ratio of return volatility to net volume provides a lower bound on price impact, conditional on the level of investor heterogeneity. Using several measures from survey and portfolio data, we document substantial investor heterogeneity, implying meaningful lower bounds on price impact. The bounds align closely with reduced-form estimates from a variety of quasi-experiments, such as price impacts from index reconstitutions, whereas traditional impact measures based on gross trading volumes perform poorly. Our bounds prove particularly useful in settings where event study evidence is difficult to obtain: we demonstrate how they vary over time, across individual assets, and at various levels of aggregation, including the aggregate stock market, and discuss their implications for asset pricing models and the macro structure of liquidity.
The recent literature on demand-system asset pricing estimates the slope of investors’ demand curves from static logit regressions, implying counterfactual experiments have constant impacts that do not revert over time. Using investors’ trades at different horizons, I provide reduced-form evidence that elasticities increase significantly in the long run. To capture these dynamics structurally, I propose a partial adjustment model differentiating between short- and long-run elasticities. I find that the price impact from counterfactuals is four times larger at quarterly horizons than in the long-run equilibrium. The model accounts for investor inertia, captures long-run reversal, and provides insights into return predictability.
Many active funds hold concentrated portfolios. Flow-driven trading pushes up the funds’ existing positions resulting in self-inflated returns. We show that when allocating capital across funds, investors are unable to identify whether realized returns are self-inflated or fundamental. Flows that chase self-inflated returns predict bubbles in ETFs and lead to a daily wealth reallocation of $500 Million from ETFs alone. We provide a simple regulatory reporting measure – fund illiquidity – which captures a fund’s potential for self-inflated returns.
Wall Street Journal: Hot Funds and the Curse of Self-Inflated Fund Returns
Morningstar: Beware of the 'Ponzi funds' that are hiding in plain sight
Yahoo Finance: ETF Ponzi funds: How market bubbles are born every day
R&R at Journal of Finance
I show that the recent returns to ESG investing are strongly driven by price impact from flows towards ESG portfolios. Using data on trades, I estimate the market’s ability to accommodate ESG flows, which is given by the elasticity of substitution between ESG and other stocks. I show that every dollar flowing towards a represen- tative ESG portfolio increases the market value of ESG stocks by $0.8. The growing institutional flows into the ESG portfolio are the main driver of ESG returns and have caused an annual flow-driven return of 1.9%. In the absence of flows, ESG stocks would not have outperformed the market from 2012 to 2023.
Winner of the Swiss Finance Institute Best Paper Doctoral Award 2022
Media Coverage: Bloomberg (a), Bloomberg (b), Risk.net, SEC
We quantify the impact of Robinhood traders on the US stock market in a structural model. Robinhood traders account for 18% of the variation in stock returns in the second quarter of 2020. Without the surge in retail trading activity the aggregate market capitalization of small stocks would have been 20% lower.
Winner of the Swiss Finance Institute Best Paper Doctoral Award 2021
Media Coverage: The Wall Street Journal, Morningstar, Bloomberg, Risk.net, Handelsblatt (a) , Handelsblatt (b), Market Watch, Institutional Money
Returns and flows exhibit a high contemporaneous correlation. How can we identify whether returns are driven by flows or flows simply respond to contemporaneous returns? We develop a simple equilibrium model that disentangles the two channels and leads to an explicit bias-correction formula.