 | London Business School
Regent's Park London, NW1 4SA United Kingdom alines@london.edu |
About Me
I am a finance PhD candidate at London Business School (LBS), working in the areas of empirical asset pricing, financial intermediation, information economics, and market microstructure. I will be on the 2017 academic job market and will be available for interviews at the ASSA meetings in Chicago.
Working Papers
Presentations:
FMA Doctoral Consortium (2016); HEC PhD Conference (2016); AQR (2016, scheduled)
I am a finance PhD candidate at London Business School (LBS), working in the areas of empirical asset pricing, financial intermediation, information economics, and market microstructure. I will be on the 2017 academic job market and will be available for interviews at the ASSA meetings in Chicago.
Working Papers
(Job Market Paper)
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)
Stocks
purchased by institutional investors earn significant positive alpha
that declines gradually over twelve months. Using a comprehensive new
dataset of daily trades, we find that purchases predict alpha even after
controlling for stock characteristics. Observed trading behaviour
suggests that fund managers act strategically to minimize information
leakage, consistent with Kyle-type models—managers build positions in
small increments proportional to remaining alpha, 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. Alpha is negatively related to
competition/correlation, also consistent with the strategic trading
hypothesis.
Presentations: 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.