I read about this strategy in an article titled "The Low-Close Edge," which appeared in the December 2012 issue of Active Trader.  The article was written by Nat Stewart.  Stewart introduced a very simple, systematic, long-only equity strategy that generated remarkable returns, while only being invested in the market a fraction of the time.  I was so surprised by the 44% return reported in 2008, that I programmed the strategy in AmiBroker and tested it myself.  My results were even higher!

The Low-Close Strategy

Stewart presented several different versions of the low-close strategy in the article, but I will focus exclusively on the one with the highest reported returns.  The concept is simple: buy the SPY when it closes in the bottom 50% of its daily range AND in the bottom 50% of its 5-day range.  When it closes in the top 50% of its daily range OR in the top 50% of its 5-day range, liquidate the SPY position and return to cash.  It doesn't get much simpler than that.

Figure 1: Low-Close Annual Returns

Stewart reported the 2005-2012 (through 9-19-2012) SPY buy and hold and low-close "1-5-50" returns in the Active Trader article.  The 1-5-50 represents the 1-day range, the 5-day range, and the bottom 50% decision parameters.  The returns from the article are listed in the first two columns in Figure 1.

My AmiBroker returns for the SPY 1-5-50 strategy from 1994-2012 are provided in the third column.  As you can see, the returns are very similar to those reported in the article.  I used daily data for the SPY, back-adjusted for dividends.

Finally, I decided to optimize the strategy by adjusting the longer-term look-back period and the buy/sell range threshold.  The optimal parameters were 1-8-51: purchases were made when the SPY closed in the bottom 51% of its daily range AND in the bottom 51% of its 8-day range.   All strategy returns exclude transaction costs and assume a 0% return on cash investments.

Remarkably, the low-close models only had one negative calendar-year return in the entire 19-year historical period.  Neither of the low-close strategies earned negative calendar year returns in either of the last two recessions.

Figure 2: Low-Close Strategy Summary Statistics

Looking at annual returns is interesting, but it is much more instructive to look at the summary statistics for the entire 1994-2012 period (Figure 2). As you would expect, the optimized strategy dominated the un-optimized strategy in virtually every category, but the parameters were not radically different.  The optimized strategy used a slightly longer look-back period (eight day versus five days) and the thresholds were very similar (51% versus 50%).

Let's focus on the optimized strategy.  The 19-year compound annual return of 11.34% was attractive, but was even more remarkable considering the strategy was only invested 12.56% of the time.  The resulting risk adjusted return was 90.31%, which was a highly efficient use of capital.

The strategy would have suffered a drawdown of 21.86%, which led to a CAR / Max Drawdown ratio of only 0.52%.  I would prefer to see this value above 1.0.  The profit factor of 1.92 was also lower than I would like, but clearly demonstrated excess returns.  In addition, the Sharpe ratio of 1.86 was very attractive.

Practical Considerations

While the low-close strategy generated higher returns than buy and hold investing with much lower risk, there are several issues to consider before employing this type of approach.  First, I would never consider a strategy that did not have a maximum stop-loss limit on every trade, especially one that trades against the trend.

Without a maximum stop-loss amount, it would be impossible to calculate the required position size to limit losses to a desired percentage of total capital.  I tested the strategy with various percentage stop-loss levels and the results were invariably worse than those reported above, but I would still not consider trading any strategy without proper risk management.

Second, this strategy would be very difficult to trade from a psychological perspective.  It is much easier to trade with the trend: buying when the market advances and selling when the market declines.  While I acknowledge the above returns are compelling, most traders would find it very difficult to buy when the market is at the low end of its 1-day and 8-day ranges, especially during an extreme bear market like 2008.

Finally, the above returns do not include transaction costs and the strategy was very active, generating 706 round-trip trades during the testing period.  The strategy only earned 0.30% per trade before transaction costs. The bid-ask spread and commissions would have reduced this number even further.

While I do not plan on trading this strategy, it did open my eyes to the surprisingly strong performance of the market after short-term pullbacks.  It definitely warrants additional research.

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