Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Conventional, neoclassical economics assumes perfectly rational agents (firms, consumers,
investors) who face well-defined problems and arrive at optimal behaviour consistent with …

We develop a framework to theoretically and empirically analyze the fluctuations of the
aggregate stock market. Households allocate capital to institutions, which are fairly …

Agent-based modeling (ABM) is a novel computational methodology for representing the
behavior of individuals in order to study social phenomena. Its use is rapidly growing in …

Financial services generate a huge volume of data that is extremely complex and varied.
These datasets are often stored in silos within organisations for various reasons, including …

Synthetic time series are often used in practical applications to augment the historical time
series dataset, amplify the occurrence of rare events and also create counterfactual …

This book exposes and comments on the consequences of Reg NMS and MiFID on market
microstructure. It covers changes in market design, electronic trading, and investor and …

Market simulation is an increasingly important method for evaluating and training trading
strategies and testing" what if" scenarios. The extent to which results from these simulations …

In financial markets, the market-order sign exhibits strong persistence, widely known as the
long-range correlation (LRC) of order flow; specifically, the sign autocorrelation function …

Multi-agent market simulators usually require careful calibration to emulate real markets,
which includes the number and the type of agents. Poorly calibrated simulators can lead to …