Optimizing a basket against the efficient market hypothesis

TL;DR

This paper demonstrates that it is possible to construct stock baskets with specific autocorrelation properties at short time scales, challenging the efficient market hypothesis and enabling some degree of forecasting.

Contribution

The study reveals that stock baskets can be systematically designed to violate the efficient market hypothesis at short time scales, showing persistent autocorrelation structures.

Findings

Baskets with non-trivial autocorrelation can be formed at tens of seconds.

Such autocorrelation structures are persistent enough for forecasting.

Challenges the assumption of market efficiency at short time scales.

Abstract

The possibility that the collective dynamics of a set of stocks could lead to a specific basket violating the efficient market hypothesis is investigated. Precisely, we show that it is systematically possible to form a basket with a non-trivial autocorrelation structure when the examined time scales are at the order of tens of seconds. Moreover, we show that this situation is persistent enough to allow some kind of forecasting.