The “factor zoo” is a widely known phenomenon: a whole lot of published anomalies that fail out of sample. ADIA Lab researchers point to a more subtle and dangerous problem: the “fata morgana factor.” It comes not from data mining, but from models which can be misspecified despite being developed in response to the econometric canon taught in textbooks.
Models with colliders are of particular concern, as these often also exist as accurately specified models. The econometric canon favors such mis-specified models, confusing higher fit with correctness.
In an element model with a collider, the return value is ready before the worth of the collider. As a result, the stronger association resulting from the collider can’t be monetized. The profits promised in these scientific papers are a mirage. In practice, this methodological error has billions of dollars in consequences.
For example, consider two researchers estimating a high quality rating. One of the researchers examines profitability, leverage and size; the opposite adds return on equity, a variable influenced by each profitability (the factor) and stock performance (the final result).
By including a collider, the second researcher makes a false connection: prime quality now correlates with high returns prior to now. In the backtest, the second model appears to be superior. In live trading the tables are turned, the backtest is a statistical illusion that silently siphons away capital. For individual managers, these mistakes can silently reduce returns; For markets as an entire, they distort capital allocation and create inefficiencies on a worldwide scale.
