Does Basel II destabilize financial markets? An agent-based financial market perspective

The highly intelligent virtual agents for modeling financial markets

“…Our iAgent design based on Principle 29 2 may offer a solution to this problem, since the internal parameters are of iAgents' own concern. 30 …”

“…27 To sum up, through our own study of agent-based modeling and by reviewing literatures on ABMs of financial markets, 28 we found that a higher intelligence level of virtual agents is one of the key factors, which could enable such models to 29 achieve greater accomplishments in both academic and engineering aspects. Borrowed from related research fields, we re- 30 defined the concept of intelligence for traders in financial markets, namely, the abilities of information processing, learning 31 and adaptation. Based on this definition, we further proposed three principles for the design of highly intelligent agents 32 called iAgents in the modeling of financial markets.…”

“…Here, we make a second assumption: 28 the same bit value (i.e., −1, 0, or +1) at different ticks in a certain information sign series has the same directional influence 29 on the change of an iAgent's stock position. However, the influence strength should decay with the time, which is a common 30 phenomenon observed in the real world [46]. This second assumption can be understood more easily with the following 31 example: suppose that the stock price of Apple Inc. rose consecutively on both Monday and Tuesday this week; if the 32 upward stock price movement on Monday gave an iAgent a signal to increase the stock position, the upward price change 33 on Tuesday would direct him/her to do the same thing; the only difference is that the price movement of Apple stock on 34 Tuesday would have a stronger signal influence on the iAgent's decision of position adjustment.…”

Learning is neither sufficient nor necessary

“…These models hold great promise for building regulatory frameworks and market institutions that are truly robust since they enable us to perform stress tests which incorporate the effects of, for example, behavioural biases or nonlinear emergent phenomena. Such models have been used, for example, to analyse the possible effects of different regulatory frameworks such as Basel II [7], Tobin taxes [17] and the effects of intervention by central banks [23] on exchange rate fluctuations. However there are still a number of methodological issues which have prevented such models from being accepted by the mainstream economics and finance communities.…”

Disclosure Requirements, the Release of New Information and Market Efficiency: New Insights from Agent-Based Models

“…For instance, Westerhoff (2003) explored the consequences of trading halts, Scalas et al (2005) insider trading and fraudulent behavior, He and Westerhoff (2005) price caps, Wieland and Westerhoff (2005) central bank interventions, Westerhoff and Dieci (2006) transaction taxes, Brock et al (2009) hedging instruments, Hermsen (2009) Basel II regulations for market risk and Thurner et al (2009) leverage effects. All in all, these approaches seem to enable us to improve our understanding of how certain regulatory policy measures function (for a survey, see Westerhoff 2008).…”