 | Columbia Business School Uris Hall, 3022 Broadway New York, NY, 10027
United States of America anton.lines (at) columbia (dot) edu |
About Me
I joined Columbia Business School as an assistant professor of finance in July 2017. I currently work in the areas of empirical asset pricing, asset management, information economics, and market microstructure.
Working Papers
Presentations: 2017: NBER
Asset Management, EFA, Chicago, Columbia, UNC Chapel Hill, Michigan, Ohio
State, Maryland, Washington (Seattle), Boston College, LSE, Rochester, Toronto,
Notre Dame, Boston University, Colorado Boulder; 2016: FMA Doctoral Consortium, HEC PhD Conference, AQR
Work in Progress
I joined Columbia Business School as an assistant professor of finance in July 2017. I currently work in the areas of empirical asset pricing, asset management, information economics, and market microstructure.
Working Papers
The incentive contracts of delegated investment managers may have unintended negative consequences for asset prices. I show that managers who are compensated for relative performance optimally shift their portfolio weights towards those of the benchmark when volatility rises, putting downward price pressure on overweight stocks and upward pressure on underweight stocks. In quarters when volatility rises most (top quintile), a portfolio of aggregate-underweight minus aggregate-overweight stocks returns 3% to 8% per quarter depending on the risk adjustment. Prices rebound in the following quarter by similar amounts, suggesting that the changes are temporary distortions. Consistent with the growing influence of asset management in the US equity market, the distortions are stronger in the second half of the sample, while placebo tests on institutions without direct benchmarking incentives show no effect. My findings cannot be explained by fund flows and thus constitute a new channel for the price effects of institutional demand. The effects come into play precisely when market-wide uncertainty is rising and distortions are less tolerable, with implications for the real economy. Additionally, the paper offers novel evidence on a prominent class of models for which empirical investigations have been relatively scarce.
(with R. Di Mascio and N. Naik)
Using a novel sample of professional asset managers, we document positive incremental alpha on newly purchased stocks that decays over twelve months. While managers are successful forecasters at these short-to-medium horizons, their average holding period is substantially longer (2.2 years). Both slow alpha decay and the horizon mismatch can be explained by strategic trading behavior. Managers accumulate positions gradually and unwind gradually once the alpha has run out; they trade more aggressively when the number of competitors and/or correlation among information signals is high, and do not increase trade size after unexpected capital flows. Alphas are lower when competition/correlation increases.
Presentations: AFA (2019); HEC Paris (2018); SFS Finance Cavalcade (2016); FMA (2016); Jackson Hole (2015); AFFI/EUROFIDAI Paris December Finance Meeting (2015); Luxembourg Asset Management Summit (2015); EFA (2014); Trans-Atlantic Doctoral Conference (2014); INSEAD PhD Conference (2014)
Trade-Based Performance Measurement
(with R. Di Mascio and N. Naik)
We propose new metrics for investment performance based on short-run trading profitability. Since investment opportunities are scarce and value-relevant information decays over time, marginal decisions made by fund managers (i.e., trades) should provide more accurate signals about underlying skill than portfolio alphas, which are contaminated by the returns on "stale" positions. Our measures range from the very simple ("hit rate", or the fraction of trades that outperform the benchmark over the subsequent month) to the more complex (regressions relating trade size to subsequent profitability). We examine the validity of these measures in a global sample of long-only equity funds, for which we observe daily trading activity. In our sample, trade-based metrics are more persistent than portfolio alphas and, more importantly, are better able to forecast future portfolio alphas (in a mean squared error sense). Simple and complex methods are almost equally effective. A hypothetical manager-selection exercise reveals that trade-based performance measurement can improve the risk-adjusted returns to investors by up to 3% per annum.
Work in Progress
Ask EDGAR: The Informational Content of Mutual Fund Prospectuses (with S. Abis)
Learning From Soft Information: Capital Allocation in the Mutual Fund Industry (with S. Abis)
The Impact of Mutual Fund Regulation on Allocative Efficiency (with S. Abis)
Learning to Trade (with R. Lewis)