  It is a common misconception that in order to make consistent profits as a
trader, one needs to posses some extra information leading to an asset value
estimation more accurate than that reflected by the current market price. While
the idea makes intuitive sense and is also well substantiated by the widely
popular Kelly criterion, we prove that it is generally possible to make
systematic profits with a completely inferior price-predicting model. The key
idea is to alter the training objective of the predictive models to explicitly
decorrelate them from the market, enabling to exploit inconspicuous biases in
market maker's pricing, and profit on the inherent advantage of the market
taker. We introduce the problem setting throughout the diverse domains of stock
trading and sports betting to provide insights into the common underlying
properties of profitable predictive models, their connections to standard
portfolio optimization strategies, and the, commonly overlooked, advantage of
the market taker. Consequently, we prove desirability of the decorrelation
objective across common market distributions, translate the concept into a
practical machine learning setting, and demonstrate its viability with real
world market data.
