The effect of social influence in individuals’ choices and decisions is well-known and has been extensively studied in the social sciences. The recent emergence of social media and our ability to collect and analyse large amounts of data makes more quantitative, and in particular data-driven, methods more amenable. This interdisciplinary project will use mathematical and quantitative approaches to study the effects of social influence on financial market behaviour, aiming to answer such questions as "Can we measure the effect of social networks on financial decision-making?”, "Can we anticipate the market reaction to the spreading of information, or misinformation?” and "What are possible strategies for minimising the effect of malicious misinformation attacks?"
We will look at how social networks – both at the individual and societal level – influence investment decisions, and investigate whether social influence can lead to market instability. We will further consider whether it is possible to detect social influence and incipient instability from market data in real time.
We will model both financial markets and social networks using a mixture of agent-based modelling, network dynamics and stochastic differential equations, and use data assimilation to update models on the fly, with the ultimate aim of automating the detection of market instability and its causes. The stochastic models developed will be based on research in the sociology of crowd behaviour, thus linking qualitative and quantitative theories of crowd dynamics.
The overall aim of this project is to increase our understanding of the influence of social media on financial markets and inform policy makers and financial institutions.
Closing Date: 31 Mar 2019
Post Type: PhD Studentship (Funded)
