Nasdaq had to consider decimalization and its impacts in 1998. Nasdaq had to consider decimalization and its impacts in 1998

Nasdaq had to consider decimalization and its impacts in 1998.

• Nasdaq had to consider decimalization and its impacts in 1998.

• How reducing the tick size may affect the market behavior? Why should it have any effect?

• How a change to decimals can be modeled?
• What is the mechanism through which changed tick size would affect the market?
• Via individual market participants (agents) decisions?
• Via changes to market infrastructure– order matching?
• Given specific mechanisms, what other (second order?) effects may occur?

Investigate effects of possible policy and environment changes:

• Investigate effects of possible policy and environment changes:

• Ex: Evaluate the effects of changing the tick size (decimalization) and of parasitism

• Evaluate the influence of market rules and structure on market dynamics and strategies

• Demonstrate that that simulated market participants and aggregate market parameters are “sufficiently similar” to those in the real world to validate model empirically

By definition, almost tautologically, “the market” is an agent-based system.

• By definition, almost tautologically, “the market” is an agent-based system.

• The question is what predictive power such a model representation has? For what kinds of problems?

• What are the theoretical reasons for agent-based representation to work?

• Crucial differences from existing approaches:

• Modeling processes and mechanisms, rather than the outcomes and states.
• Heterogeneous rather than homogenous model
• Focus on emergent behaviors – potentially, counter intuitively, invalidating the initial model.

• Nasdaq decimalization study: an empirical example.

• Study done during 1998-2000.
• Decimalization occurred in April 2001.

Market makers

• Market makers

• Investors

• Market Agent Features:

• Autonomous
• Adaptive/learning/handcrafted strategies
• Various levels of sophistication/adaptability/ access to information

Market agents are trading in a single stock

• Market agents are trading in a single stock

• Investors have a price target which follows a Poisson process, random walk, etc.

• Investors:

• Receive noisy information about this target
• Decide whether to trade by
• Comparing this target with available price
• Incorporating market trends
• Performing sophisticated technical trading, etc.

• Market makers:

• Receive buy and sell orders
• Must learn how to set their quotes profitably

Parasitic strategy:

• Parasitic strategy:

• Attempts to undercut the current bid/offer by a small increment (tick size)
• Is not a major source of liquidity for the market

Trading in:

• Trading in:

• Market orders
• Limit orders
• Negotiated orders

• Market rules/ parameters:

• Order handling rules
• Tick size, etc.

Calibrated the model to

• Calibrated the model to

• Individual strategies
• Aggregate market parameters

• Simulated strategies are able to replicate the real-world ones (with precision up to 60-70%)

• Created self-calibrating software to use data as it comes in

Effects of tick-size changes and parasitism

• Effects of tick-size changes and parasitism

• Market dynamics effects:

• Presence and origin of “fat tails”
• Spread clustering and its causes

• Effects of market maker and investor learning and strategy evolution

As tick size is reduced, parasitic strategies increasingly impede price discovery / market’s ability to generate useful information

• As tick size is reduced, parasitic strategies increasingly impede price discovery / market’s ability to generate useful information

“Fat tails”:

• “Fat tails”:

• A large probability of extreme events by comparison with a Gaussian distribution

• Origins are uncertain

• Herd effects, other?

• Our model generates fat tails with no herd effects

frequency

• frequency

The fat tails seem to disappear when the data points are taken far apart (50 periods here)

• The fat tails seem to disappear when the data points are taken far apart (50 periods here)

Possible explanations:

• Possible explanations:

• Interaction and self-interaction through price
• Existence of spread
• Memory of traders, investors, etc.

• No explicit “herd” effects included

Nasdaq dealers collusion accusations -Christie and Schulz (1994)

• Nasdaq dealers collusion accusations -Christie and Schulz (1994)

• SEC investigation into quoting behavior on Nasdaq (1996) and subsequent settlement

• Clustering in various financial markets - Hasbrouck (1998)

Spread = difference between smallest offer and largest bid

• Spread = difference between smallest offer and largest bid

• Spread clustering occurs when some spread values occur much more frequently than others

Emergent property in the simulation: no collusion is present, yet the spread clustering occurs

• Emergent property in the simulation: no collusion is present, yet the spread clustering occurs

• Real-world issue: Nasdaq, Forex

Spread Learning market maker is the most profitable dealer on the market under many circumstances

• Spread Learning market maker is the most profitable dealer on the market under many circumstances

• Known exceptions: high volatility, tough parasites

Not only the participants strategies change, but the market institutions change as the result.

• Not only the participants strategies change, but the market institutions change as the result.

Systematising the work

• Systematising the work

• Best features of ABMs combined with best features of more traditional approaches.

• ABMs to explain / derive parameters of stochastic processes in finance. For example, what features of agents / institutions give raise to specific market behaviors?

• How strongly macro laws are coupled with the micro laws?

• Is it possible to have the same macro behaviors when micro behaviors are different?

Predicting complex outcomes is possible!

• Predicting complex outcomes is possible!

• Building and validating any “big ABM model” such as this is difficult and time-consuming

• Machine learning agents were an important part of that.

• Ensuring sufficient accuracy and rigour is very difficult. Careful involvement and feedback from market participants/regulators was essential – one benefit of this being (expensive) paid consulting work was that this was not optional

• Any questions?