For more information about this meeting, contact Kris Jenssen, Yuxi Zheng.
|Title:||Statistical Arbitrage in the U.S. Equities Market|
|Seminar:||Computational and Applied Mathematics Colloquium|
|Speaker:||Marco Avellaneda, Courant Institute, New York University|
|We study model-driven statistical arbitrage strategies in
U.S. equities. Trading signals are generated in two ways:
using Principal Component Analysis and using sector ETFs.
In both cases, we consider the residuals, or idiosyncratic
components of stock returns, and model them as a mean-reverting
process, which leads naturally to "contrarian'' trading signals.
The main contribution of the paper is the back-testing and
comparison of market-neutral PCA- and ETF- based strategies
over the broad universe of U.S. equities. Back-testing shows
that, after accounting for transaction costs, PCA-based
strategies have an average annual Sharpe ratio of 1.44 over the
period 1997 to 2007, with a much stronger performances prior
to 2003: during 2003-2007, the average Sharpe ratio of PCA-based
strategies was only 0.9. On the other hand, strategies based
on ETFs achieved a Sharpe ratio of 1.1 from 1997 to 2007, but
experience a similar degradation of performance after 2002. We
introduce a method to take into account daily trading volume
information in the signals (using "trading time'' as opposed to
calendar time), and observe significant improvements in
performance in the case of ETF-based signals. ETF strategies
which use volume information achieve a Sharpe ratio of 1.51
from 2003 to 2007.
The paper also relates the performance of mean-reversion
statistical arbitrage strategies with the stock market cycle.
In particular, we study in some detail the performance of the
strategies during the liquidity crisis of the summer of 2007.
We obtain results which are consistent with Khandani and Lo
(2007) and validate their "unwinding'' theory for the quant
fund drawndown of August 2007.|
Room Reservation Information
|Date:||10 / 23 / 2009|
|Time:||03:35pm - 04:25pm|