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5 Moving Average Signals That Beat Buy and Hold: Backtested Stock Market Signals

by Steve Burns and Holly Burns (2017)

Quick summary -- an in-depth PhD-level extended summary (10-30 pages) for this book is coming soon.

5 Moving Average Signals That Beat Buy and Hold: Backtested Stock Market Signals

by Steve Burns and Holly Burns

Overview

Published in 2017, this concise book presents five mechanical trading systems based on moving averages, all backtested on the S&P 500 ETF (SPY). The authors argue that simple, rule-based trend following systems can match or exceed buy-and-hold returns while dramatically reducing maximum drawdowns and emotional stress.

The Five Systems

  1. 200-Day SMA (Monthly Signal): Buy when price closes above the 200-day SMA at month-end; sell when it closes below. From 2000-2016, this system returned 317.7% versus 125.2% for buy-and-hold, with a maximum drawdown of only 17.3% compared to 55.2%.

  2. 250-Day SMA (Daily Signal): Uses a longer 250-day moving average with daily signals to avoid false breakouts. Returns approximately matched buy-and-hold but with less than half the drawdown (23.1% vs 55.2%).

  3. Golden Cross/Death Cross (50/200-Day): Buy on the golden cross (50-day crosses above 200-day); sell on the death cross. A well-known institutional signal.

  4. Dual Moving Average Crossover: A crossover system using two specific moving averages for entry and exit signals.

  5. Basic Trend Following: A system designed to stay long during bull markets and move to cash during bear markets using price relative to a key moving average.

Key Arguments

  • 80% of actively managed mutual funds fail to beat the S&P 500 index over the long term.
  • The emotional factor causes most investors to buy high and sell low. Mechanical systems remove emotion.
  • The S&P 500 index itself functions as a trend-following system by adding winners and dropping losers.
  • Moving average systems should create asymmetric risk/reward: small losses on false signals, large gains from captured trends.
  • Systems are reactive (based on price reality), not predictive (based on opinions).

Limitations Acknowledged

The authors note that backtesting time frames influence perceived effectiveness, and that all systems experience whipsaws during range-bound markets. Past performance does not guarantee future results.