Simulation-driven experimental hypotheses and design: a study of price impact and bubbles

Abstract

A crucial aspect of every experiment is the formulation of hypotheses prior to data collection. In this paper, we use a simulation-based approach to generate synthetic data and formulate the hypotheses for our market experiment and calibrate its laboratory design. In this experiment, we extend well-established laboratory market models to the two-asset case, accounting at the same time for heterogeneous artificial traders with multi-asset strategies. Our main objective is to identify the role played in the price-bubble formation by both self-impact (i.e., how trading orders affect the price dynamics) and cross-impact (i.e., the price changes in one asset caused by the trading activity on other assets). To this end, we vary across treatments the possibility of traders of diverting their capital from one asset to the other, thereby artificially changing the amount of liquidity in the market. To simulate different scenarios for the synthetic data generation, we vary along with the liquidity the type of trading strategies of our artificial traders. Our results suggest that an increase in liquidity increases the cross-impact, especially when agents are market-neutral. Self-impact, however, remains significant and constant for all model specifications.