Work

The adaptive markets hypothesis through the lens of machine learning

Year: 2025

Type: article

Abstract: The adaptive markets hypothesis (AMH) postulates that markets are not stationary environments permanently in equilibrium as the efficient market hypothesis (EMH) posits, but evolve over time. However, testing this requires tools that capture the inherent complexity of financial markets. Therefore, in this paper we introduce an approach that uses the Long Short-Term Memory (LSTM) model to capture changes in the structure of the economic-financial environment. We apply the methodology to the U.S. equity market represented by the S&P500 for 40 years of daily data. With this tool we will demonstrate that, even with non-heuristic approaches such as the LSTM model, the learning and adaptation process after a change in the environment is slow, which is consistent with the AMH.

Source: Heliyon

DOI: 10.1016/j.heliyon.2025.e44280

Authors: Julio Villavicencio, Oscar Miranda, Edwin Villanueva

Institution: Pontificia Universidad Católica del Perú

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: Pontificia Universidad Católica del Perú