Golden Cross

Below the long-only golden cross timing strategy in adjusted SPY ETF data since inception:

Buy if MA(f) > MA(s) Sell is MA(s) < MA(f)

The strategy is long when d > 0 and exits when d < 0. Algo: d = MA(f) — MA(s)

where f and s are the periods of the fast and slow moving averages, respectively. popular choice for the moving average periods: f = 50 and s = 200

The timing strategy outperforms buy and hold on a both absolute and risk adjusted basis. Annualized return for the strategy net of commission of $0.01/share is 9.42% versus 9.36% for buy and hold while maximum drawdown is -19.45% versus -55.19%, a significance improvement. This results in a MAR (CAGR/Max. DD) of 0.48 for the timing strategy versus 0.17 for passive buy and hold. vary the fast moving average f from 5 to 50 and the slow moving average s from 100 to 500, both in increments of 5, and consider all the strategies resulting from the various combinations we find out that an overwhelming percentage of them, nearly 98%, outperform buy and hold on a MAR basis. Actually, the f = 50, s=200 choice results in median MAR performance.

In passive investing there are hidden features that are used in support of this strategy.

Markets tend to rise over the longer-term Drawdown never reaches 100% Recoveries from maximum drawdown are sufficiently quick In fact, whereas a golden-cross strategy might have been the outcome of brute force optimization or some crude machine learning or other quantitative method, passive investing appears to be the outcome of mental learning, or a hybrid approach, known as quantamental in modern terminology. However, passive investors usually do not understand, or underestimate, the impact of the hidden features. For example, if in the future recovery from a drawdown in the order of 50%, similar to the one generated by the financial crisis, takes many years, let us say 10 or even longer, then many passive investors will be forced to sell at a large loss to cover their living expenses.