Value and momentum strategies both exploit investors’ emotions. But why is value looking so glum?
If the stockmarket were efficient, then over the long run no one could beat it. The good news for active investors is that it’s not efficient – it’s made up of human beings, who make predictable misjudgements, creating opportunities for attentive investors to exploit. Two strategies (or “factors” – see box below) with a long track record of outperforming are “value” and “momentum”. A recent paper from US money manager O’Shaughnessy Asset Management (OSAM) delves deep into both factors, and concludes that they are both driven by the same thing – investors’ overreactions.
Value investors benefit from overreaction to the downside. A company does badly, and investors shun it, sending the share price to unjustifiably low levels. If you buy in cheaply, you can benefit when the market realises its error – or as the OSAM paper puts it: “Relative to actual realised future earnings, the market imposes an excessive discount on value stocks.” Between 1964 and 2017, if you had bought the cheapest fifth of US large caps (judged by the price/earnings ratio) each year, then you’d have made more than 13% annually, compared with 9.8% for the wider S&P 500.
Momentum investors, on the other hand, profit from overoptimism. Investors expect companies that have recently done well to do even better in the near future. OSAM notes that, again, between 1964 and 2017, if you had invested in the top fifth of large-caps judged by their share-price returns over the prior six months, you’d have made about 12.5% a year.
By temperament, MoneyWeek is more attuned to value investing (we like our investment case to be underpinned by fundamentals). However, the strategy has endured a long period of underperformance, to the point where a Wall Street Journal headline this week declared: “Value investors face existential crisis”. However, in OSAM’s view, the main issue is not that value is “broken” – it’s simply that an unusually large number of value stocks have proved to be value “traps” in the wake of the financial crisis. In other words, they’ve been cheap for a reason, and they haven’t recovered – which is key to value’s performance.
This makes sense. Financial stocks – most of which fall into the value category just now – were at the heart of the 2008 crisis, and have taken an incredibly long time to recover (mainly because many of them would have gone bust were it not for the support of central banks). Meanwhile, another value stalwart – the resources sector – has been through a brutal bust in recent years. Yet many of these stocks should start to do better in an environment of rising interest rates and inflation. So don’t give up on value yet.
I wish I knew what a factor was, but I’m too embarrassed to ask
In the jargon, a “factor” is a characteristic that has been shown to contribute to market outperformance. Research into factors was largely driven by academics trying to figure out why certain stocks tended to generate higher returns than theories about efficient markets would have predicted.
For example, widely accepted factors include size (the observation that small companies tend to beat large firms over time); value (cheap companies beat expensive ones); yield (high-yielding stocks do better than low-yielding ones); and momentum (stocks that go up just keep on going up). To be clear, these factors will not always beat the market over any given time period – but looking at historical data over the long run, they have generated superior returns in many different global markets.
“Smart beta” is one trend in the investment industry that tries to exploit both the rapid growth in computing power and the growing sense of disillusionment with “active” fund managers. Smart-beta exchange-traded funds (ETFs) promise to use computer algorithms to construct and invest in indices based on various factors. So you might invest in a “momentum“ ETF that continually rebalances into momentum stocks, or a “value” ETF that does the same for stocks viewed as cheap on certain measures.
One problem with the race to find new factors for smart beta to exploit is the risk of “data mining” – if you look hard enough at historical data, you can find apparently meaningful patterns that are in fact simply statistical flukes. A 2017 paper by Kewei Hou and Lu Zhang of Ohio State University, and Chen Xue of the University of Cincinnati, found that the vast majority of 447 “anomalies” found by equity-market researchers were precisely that. However, it’s fair to say that the best-established factors (such as those above) are widely accepted as true.