My previous article addressed the issue of ETF and index backtesting (a form of “statistical marketing”) and why the average investor should be aware of what backtests entail, how they can differ from actual returns and how one can objectively consider them.

Backtests share common statistical metrics; Standard Deviation, Sharpe Ratio, Beta, Alpha, R-Squared, Maximum up/down draw, The point of these metrics are to explain how the hypothetical index performs, and more importantly, how much risk is associated with the backtest. 

Standard Deviation: one of the more basic risk measures, it shows how much the index fluctuates above or below its average. The higher the number, the more volatile the ETF.

Sharpe Ratio: A simple ratio, which shows the return divided by the standard deviation of the index. Typically, you want a ratio which is above 1, or rather, where your risk/return is at parity. Anything greater and you are receiving a greater return with lower risk. Anything lower than 1, you are receiving a lower return per risk taken.

Beta: The correlation of the index to a benchmark, i.e. the S&P 500 (Ticker: SPY). The higher the beta, the more the index moves with the overall markets. A beta of 1 means the index moves in perfect sync with the benchmark. Low beta means the index has not moved in tandem with the overall markets, and a negative beta means the index has moved opposite the benchmark. I.e. the technology sector (Ticker: XLK) has a beta of 1.52. A very important fact to remember here: betas change over time.

Alpha  The amount of return the index has done in excess of the benchmark. I.e. the  financial sector (Ticker: XLF) has an alpha of -0.61.

R-Squared  This is the predictability of the index to the moves of the benchmark. If R squared is high, that means the beta of the index is more than likely accurate. If it is low, then the beta is, statistically, not accurate.

Maximum up/down draw: These explain the positive or negative trends in the index, and the benchmark it matches up to. It is important to keep these in mind, as they will likely give you insight as to the kind of losses you may have to stomach by holding the ETF. Up/down periods show the continuous number of up or down months the index/benchmark experienced, as well as the returns the index/benchmark experienced in those periods. This also can be shown for yearly data. Worst months and years are also shown, giving the investor a glimpse of what the worst percentage gains were for the backtest (or best, as the case may be).

Statistical metrics; Index compared to benchmark: 

When considering any ETF for your portfolio, one should ask some very simple questions, regardless of the backtesting data. My next article will address why it is important for an ETF to “plug a whole”, why you should take a look “under the hood” and why research without common sense makes no sense.

Set a reminder for the follow up article:

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