Readers might be interested in an article that A. Duran and I have coming out in Quantitative Finance this year entitled A Profitable Trading and Risk Management Strategy Despite Transaction Cost. In the article, we develop a strategy which outperforms the “market” in rigorous out-of-sample testing. We’ve made sure to check the robustness of the results by performing Monte Carlo simulations on both the S&P 500 and Russell 2000 while varying the subsets of stocks and time periods used in the simulation.
The strategy is interesting in that it is based on behavioral patterns. Unlike many other algorithmic trading models, our strategy is modeled after a human trader with quarterly memory who categorizes the market return distribution and market risk into low, medium, and high categories. Technically, it accomplishes this by non-parametrically categorizing windowed estimates of the first four moments of the return distribution and the normalized leading eigenvalue of the windowed correlation matrix. Based on the assessment of these low/medium/high categories and past experience in similar states, the strategy then decides whether to invest in the market index, invest in the risk-free asset, or short the market. The strategy soundly outperforms the market index in multiple markets over random windows and on random subsets of stocks.
While you’re waiting for its publication in Quantitative Finance, you might check out a copy over at SSRN. Here’s the abstract and a figure below comparing the log-return of our strategy with the market over one realization:
We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision making process by using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, switching among several investments positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for equity market without short selling for S&P 500-listed 168 stocks during the 1998-2008 period and Russell 2000-listed 213 stocks during the 1995-2007 period. Our Monte Carlo simulation over both the various set of stocks and the interval of time confirms our findings.
Do you think the strategy will stay profitable now that your result is being publicized? You should make some predictions about how it will fair in the future.
Hi Michael,
That’s always a fair question. I don’t imagine this strategy will be executed with enough magnitude to overcome other behavioral or algorithmic dynamics in the S&P500. The answer still depends on what you believe algorithmic trading’s effects on the market is and other more broad beliefs about Lucasian critiques.
As far as predicting its performance in the future, we have performed a number of simulations that “generalize” our results to other time periods that are not too dramatically unlike our training sample. Note that the method does outperform the market even through the recent equity declines in 2008 and 2009. However, there’s never a guarantee that even the best out-of-sample backtesting can prepare you for what the future might hold.
As far as predicting its performance in the future, we have performed a number of simulations that “generalize” our results to other time periods that are not too dramatically unlike our training sample. Note that the method does outperform the market even through the recent equity declines in 2008 and 2009.
Currency Trading Center
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Justin