Measuring the Impact of Passive Investing on Market Efficiency through an Artificial Stock Market

Abstract

The purpose of this thesis is to measure stock market efficiency when passive investing increases. We address this research question through a novel approach by creating an artificial stock market, which is founded on theoretical literature and empirical data. This model replicates stock market price behavior and interactions, thereby allowing us to measure effects of different passive share environments. The methodology applied to measure market efficiency is based on active investors average CAGR relative to the artificial stock market CAGR throughout multiple stochastic forecasting periods of 20 years, which then are processed through a Monte Carlo Simulation method that enables us to obtain statistical valid outputs. Our results indicate that an increase in passive investing may affect market efficiency in a high passive share environment. Moreover, we find indications that stock market volatility is not impacted when passive investing increases. We go on to consider potential practical implications of our findings on three levels, 1) Markets, 2) Regulators, and 3) Investors. Finally, we elaborate on our results in perspective to other financial markets, such as the bond market.