The Future for Factor Investing May Be Different Than its Backtested Past
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Data mining is a huge risk with factor-based investment strategies. Many factors have proven to not work in practice and even the most popular factors, like value and momentum, may prove less effective going forward. Crowding in factor strategies, changes in the economy and new business models may eliminate any potential excess return from simple screening metrics that form the basis for many factors. Investors can avoid being fooled by backtests by keeping in mind that most attempts to beat markets will fail because trading is a zero-sum activity.
There are cause and effect relationships in the world and in investing that hold true over time. Many are common sense and easily observable – like fire creates smoke – while others are harder to see and understand. With investing, true relationships can be hard to see because of randomness and noise in data, and there’s a risk we convince ourselves certain relationships exist that really do not (e.g. smoke creates fire). In much of quantitative finance, data is mined to show a certain effect, but the logic behind the cause and effect relationship is not robust. Then suddenly, because of evidence in noisy historical data, investors begin to believe that smoke creates fire.
When historical evidence disagrees with our logic, investors should favor applying a fundamental understanding over what a backtest prescribes.
Factor investing is an area I have researched and written about extensively (see here, here and here) . It is an approach to active management that is lower cost and backed by decades of historical data, compared to the standard high-cost, overdiversified stock picking approach that has failed. But it is still active management, and with active management there is a loser for every winner. That’s a fact of markets. Normally the few winners in active management leverage a few key insights that are not recognized by the masses, at least at the time. In contrast, most active management losers tend to copy each other, using the same strategies and managers.
Factor investing is, by its nature, transparent and therefore easily copied. This is why many factor investing strategies are increasingly problematic. Data mining, factor crowding, as well as economic changes are all reasons why such strategies may disappoint. Popular value and momentum strategies are used as examples throughout this thought piece to illustrate. Keep in mind, I am not trying to definitively say that such factor strategies do not work, but instead hoping potential users of these strategies will pause and ask deeper questions about them. In the end, we can never forget the unavoidable fact that trading and beating markets is a zero-sum game.