Interesting paper that discusses outlier losses experienced by long/short hedge funds during 2007 and proposes an interesting EOD RTM strategy (one I developed and tested myself prior to coming across this paper, dammit!):
“Given a collection of N securities, consider a long/short market-neutral equity strategy consisting of an equal dollar amount of long and short positions, where at each rebalancing interval, the long positions are made up of losers (underperforming stocks, relative to some market average) and the short positions are made up of winners (outperforming stocks, relative to the same market average). By buying yesterday’s losers and selling yesterday’s winners at each date, such a strategy actively bets on mean reversion across all N stocks, profiting from reversals that occur within the rebalancing interval.”
(click on title for link)
A quick study on jump diffusion models, as applied to energy commodity markets (click on title for paper):
Here’s a quick little study on a simple mean reversion technique using the SPY ETF:
…when SPY closes strong (in the top 10% of its range) but still only manages a small gain on the day, that the next day has a downside tendency
I suspect that when you factor in commissions and the fact that one cannot conditionally short the close price of any stock (MOC orders generally have to be entered >10min prior to the close), the edge might prove artificial. Interesting food for thought, though…
Interesting paper on Forex RTM / MOM strategies (click on title for paper):
“This paper implements a trading strategy combining mean reversion and momentum in foreign exchange markets. The strategy was originally designed for equity markets, but it also generates abnormal returns when applied to uncovered interest parity deviations for ten countries. I find that the pattern for the positions thus created in the foreign exchange markets is qualitatively similar to that found in the equity markets. Quantitatively, this
strategy performs better in foreign exchange markets than in equity markets. Also, it outperforms traditional foreign exchange trading strategies, such as carry trades and moving average rules.”
As a trader which would you prefer: A slow bleed of small losses with occasional big winners (ala momentum strategies) or large infrequent losses with many small winners (ala mean reversion)? This interesting 2004 paper (by none other than Taleb himself) discusses.
System A didn’t give me much to crow about for November; It ended the month flat at +0.5%. The system started the month out with some nice gains, booking over +8%, but then was spanked by the market mid month and gave them all back. The last two weeks of the month offered the system very few trades and it sat on the sidelines for nearly seven straight trading days.
In a prior post I discussed the performance of a system I’ve been trading live for over two years (and whose algobot is running as we speak). For the purposes of this discussion, I’ll refer to this system as “System A”.
As mentioned in the post, System A performs well but has a known weakness: It is very susceptible to Black Swans (outlier market shocks) and can generate some uncomfortable drawdowns when they hit. The system usually recovers from these drawdowns reasonably fast, but they’re certainly no fun to sit through when you’re trading with real $$$.
These Black Swans seem to be hitting the market with increasing frequency, so after surviving the most recent crash (August 2011), I started experimenting with a variation of System A (we’ll call it “System B”) that temporarily moves to the sidelines the moment it senses a crash is imminent.
The backtesting results of System B are encouraging. Here is the equity curve from an 8-year trading simulation run (click to zoom):