Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses

Authored by Mario Gutierrez-Roig, Carlota Segura, Jordi Duch, Josep Perello

Date Published: 2016

DOI: 10.1371/journal.pone.0159078

Sponsors: European Union Generalitat de Catalunya Fundación Española para la Ciencia y la Tecnología Secretaria d'Universitats i Recerca MINECO

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Decisions made in our everyday lives are based on a wide variety of information so it is generally very difficult to assess what are the strategies that guide us. Stock market provides a rich environment to study how people make decisions since responding to market uncertainty needs a constant update of these strategies. For this purpose, we run a lab-in-thefield experiment where volunteers are given a controlled set of financial information - based on real data from worldwide financial indices- and they are required to guess whether the market price would go ``up{''} or ``down{''} in each situation. From the data collected we explore basic statistical traits, behavioural biases and emerging strategies. In particular, we detect unintended patterns of behavior through consistent actions, which can be interpreted as Market Imitation and Win-Stay Lose-Shift emerging strategies, with Market Imitation being the most dominant. We also observe that these strategies are affected by external factors: the expert advice, the lack of information or an information overload reinforce the use of these intuitive strategies, while the probability to follow them significantly decreases when subjects spends more time to make a decision. The cohort analysis shows that women and children are more prone to use such strategies although their performance is not undermined. Our results are of interest for better handling clients expectations of trading companies, to avoid behavioural anomalies in financial analysts decisions and to improve not only the design of markets but also the trading digital interfaces where information is set down. Strategies and behavioural biases observed can also be translated into new agent based modelling or stochastic price dynamics to better understand financial bubbles or the effects of asymmetric risk perception to price drops.

Tags

Uncertainty Decision-Making Risk Model Psychology Stock-market Financial-markets Advice Trading behavior Biases