A cura di Maneesh Shanbhag, Alpha Architect
We believe 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 factor 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. For us, when historical evidence disagrees with our logic, we always favor applying our fundamental understanding over what a backtest prescribes.
Factor investing is an area we have researched and written about extensively, 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, closet-indexing, stock-picking approach, which seems to have failed. But factor investing is still active management (i.e., classic definition of active), and with active management, there is a loser for every winner (at least we think so). 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 concerning to us (and this is setting aside the trading cost debate). Data mining, factor crowding, as well as economic changes are all reasons why such strategies may disappoint in the future. We use popular Value and Momentum strategies as examples throughout this thought piece to illustrate these points. Keep in mind, we are 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.
To summarize the key points up front:
- Data mining is a huge risk with risk 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.(1)
- Investors can avoid being fooled by backtests by always keeping in mind that most attempts to beat markets will fail because trading is a zero-sum activity.
Data mining is a risk even with Value and Momentum strategies
Value is the buying of “cheap” assets, at least based on measures such as a low price-to-earnings (P/E) ratio for stocks. This is the opposite of Growth, or high P/E stocks, which are statistically expensive. Typically, the way a stock becomes relatively cheap is by underperforming in the recent past, and vice versa for Growth stocks. Momentum strategies can be compared similarly. Past winners (Momentum stocks) is about buying stocks that have recently outperformed on the basis that their trend will continue. The opposite is Past Losers (or low momentum).
The table below summarizes these four strategies and how they logically relate to each other:
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