Professor Jeremy J. Siegel walked delegates at the Equity Research and Valuation 2013 conference through a treasure trove of historical data on the financial markets. He examined the long-run performance of a wide range of asset classes, culminating in an update of his now-famous Stocks for the Long Run data set, which shows that equity risk premia are real and universal. Across huge swaths of time and across different countries, stocks consistently beat inflation. Siegel's conclusion is that stocks are the most volatile asset class in the short run - but the most stable asset class in the long run.
Of course, Siegel, a professor of finance at the Wharton School of the University of Pennsylvania, also sounded a note of caution, warning of aggregation bias in data. As an example, he cited Robert Shiller's cyclically adjusted price-to-earnings ratio (NYSEARCA:CAPE). Many have focused on the CAPE ratio to argue that stocks are richly valued, but the S&P 500 (SPX) earnings data suffer from an aggregation bias in which large losses from a few companies can offset the earnings of the rest of the index. The S&P 500 treats companies as if they were a single corporation with 500 divisions. Of course, this approach to constructing the S&P earnings is wrong, particularly when there are large losses generated by a small number of firms. Importantly, P/E ratios computed from the S&P earnings data set are problematic, and these problems are amplified when one uses CAPE to examine cycles.
The game of investing is difficult enough on its own, but investing based on bad data can lead to some very poor conclusions. This flawed data from the S&P contributes to why some feel we are in an equity bubble, as Shiller's CAPE ratio is currently well above long-run averages. However, when adjusting the data by using corporate earnings from the national income and product accounts (NIPA) and market-capitalization weighting corporate earnings, a very different picture emerges. The adjusted CAPE ratio reveals that stocks today are trading slightly below their long-run average multiple.
Of course, the risk isn't just that you get one trade wrong. The risk is that your negative experience could cause you to abandon an otherwise sound investment principle that you erroneously conclude doesn't work. In other words, the principle might be fine. It may just be your data. Check out Siegel's entire presentation in the video above.
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