Anton Lines

Assistant Professor of Finance

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 asset pricing, asset management, and machine learning.


Working Papers


Do Mutual Funds Keep Their Promises?

(with S. Abis)

Revise and Resubmit, Journal of Financial Economics

Conferences: AFA, NBER, FutFinInfo

Mutual fund prospectuses contain a wealth of qualitative information about fund strategies, yet a systematic analysis of this content is missing from the literature. We use machine learning to group together funds with similar strategy descriptions, and ask whether they act in accordance with the text. Despite weak legal recourse for investors, we find that mutual funds largely do keep their promises. We document a market-based disciplinary mechanism: when funds diverge from their group's core strategy, investors withdraw capital. Funds respond to these punitive outflows by reducing their divergence from the peer group average at a faster rate.


Alpha Decay and Institutional Trading

(with R. Di Mascio and N. Y. Naik)

Revise and Resubmit, Review of Financial Studies

Conferences: AFA, EFA, SFS Cavalcade, Jackson Hole Finance Conference, Copenhagen FRIC Conference, Luxembourg Asset Management Summit, AFFI/EUROFIDAI Paris December Meeting

We document novel facts about the term structure of institutional trading and performance using transaction-level data on professional fund managers. New stock purchases earn positive risk-adjusted returns that decay gradually over the subsequent twelve months, and managers continue to buy the same stock in small increments for as long as the alpha remains positive, with proportional intensity. Greater competition for information and more highly correlated signals are associated with more aggressive trading and lower alpha. Our findings confirm many predictions of informed trading models, but also pose some new challenges for the theoretical literature.


What Drives Trading in Financial Markets? A Big Data Perspective

(with S. Ke)

We use deep Bayesian neural networks to investigate the determinants of trading activity in a large sample of institutional equity portfolios. Our methodology allows us to evaluate hundreds of potentially relevant explanatory variables, estimate arbitrary nonlinear interactions among them, and aggregate them into interpretable categories. Deep learning models predict trading decisions with up to 86% accuracy out-of-sample, with market liquidity and macroeconomic conditions together accounting for most (66-91%) of the explained variance. Stock fundamentals, firm-specific corporate news, and analyst forecasts have comparatively low explanatory power. Our results suggest that market microstructure considerations and macroeconomic risk are the most crucial factors in understanding financial trading patterns.


Learning from Prospectuses

(with S. Abis, A. M. Buffa, and A. Javadekar)

Conferences: AFA, SFS Cavalcade, FutFinInfo, UNSW AP Workshop, Melbourne AP Meeting

We study qualitative information disclosures by mutual funds when investors learn from such disclosures in addition to past performance. We show theoretically that fund managers with specialized strategies optimally choose to disclose detailed strategy descriptions, while those with standardized strategies provide generic descriptions. Generic descriptions lead to benchmarking errors by investors who confuse factor returns and skill, resulting in higher fund flow uncertainty. While all managers dislike this uncertainty, those with above-average factor exposures also benefit from the errors on balance and thus grow larger. We find evidence for this trade-off in the data, using a comprehensive dataset of fund prospectuses: funds with more informative descriptions are smaller and more specialized, exhibit higher flow- performance sensitivity, and show lower correlation between size and flow volatility. Investors in these funds make fewer benchmarking errors, and the effects are more pronounced for funds with shorter return histories.


Do Institutional Incentives Distort Asset Prices?

Conferences: NBER, EFA

I show that fund 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 2% to 5% per quarter depending on the risk adjustment. 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.


Work in Progress


Reinforcement Learning in Asset Pricing

Reinforcement learning (RL) algorithms can be used to efficiently solve complex discrete time economic systems that are computationally too expensive for standard numerical methods. I introduce a Walrasian auctioneer into the popular Actor-Critic family of RL algorithms to allow for market clearing, and apply this new methodology to solve a dynamic equilibrium model of asset pricing under asymmetric information. The model features many assets with an arbitrary covariance structure, multiple strategic investors with heterogeneous private signals, uninformed non-strategic investors, and transaction costs. Unlike in standard strategic trading models, informed trading intensity in my model is reduced when the fraction of informed traders in the market rises, while return volatility is increased. The model generates complex trading dynamics, where investors with more precise private signals learn to front-run investors with less precise signals, leading to price overreactions and corrections despite all agents having rational expectations.


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.


A Macro-Finance Model of Carbon Pricing (with N. Clara)


Active and Passive Management: A Unified Approach (with P. Akey)