Basic trade stats done for Faber model, indices in isolation

In general, use of the 10 week SMA timing system improved returns relative to volatility (measured by Sharpe) and max drawdown over the same stats for buying and holding the same index.

There were some discrepancies in the stats that I produced with R and the ones that Faber claimed in his paper. Especially in the trade stats for trading the US Govt 10 Year Bonds (ignore the gspc label).


For the same period (1973 through 2008) Faber has a buy and hold CAGR of 8.69% and a timing CAGR of 8.79%, whereas I obtained 8.3% for B&H and only 6.5% for timing. Faber has a max drawdown of 18.79% for B&H and 11.2% for timing, but I obtained, through R, about a 16.5% max dd for both B&H and timing. Not huge discrepancies, and they seem to be less significant for the other 4 indices; quantifying instead of eyeballing the differences in stats might help uncover the cause, but at this point this is a proof of concept, not a rigorous trading system (yet), so I think I’ll move onto modeling Faber’s portfolio.

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