Forschungskolloquium 2008
Download
1 / 33

- PowerPoint PPT Presentation


  • 195 Views
  • Uploaded on

Forschungskolloquium 2008. Kommunikations-Netzwerk-Topologie und Marktverhalten 15. Februar 2008. von Oliver Hein. Contents. Frankfurt Artificial Stock Market Components Agent Types Auction Method Networks Network Topologies Network Centralization Measures Simulation Parameters

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '' - orli


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Forschungskolloquium 2008 l.jpg
Forschungskolloquium 2008

Kommunikations-Netzwerk-Topologie

und

Marktverhalten

15. Februar 2008

von

Oliver Hein


Contents l.jpg
Contents

  • Frankfurt Artificial Stock Market

    • Components

    • Agent Types

    • Auction Method

  • Networks

    • Network Topologies

    • Network Centralization Measures

  • Simulation

    • Parameters

    • Simulation Results:

      • Centralization against Volatility and Distortion

      • Agent Type Performance

  • Outlook


Frankfurt artificial stock market l.jpg
Frankfurt Artificial Stock Market

The Frankfurt Artificial Stock Market (FASM) 1.6 is available for download at:

www.finace.org

But there is no documentation yet!

Only articles exist that describe the system.




Kommunikationsnetzwerke l.jpg
Kommunikationsnetzwerke

Börse

Meistausführungsprinzip

Zufallsnetzwerk

Scale-Free-Netzwerk

Blau=kaufen, Rot=verkaufen


Fundamental agents l.jpg
Fundamental Agents

  • Fundamental agent k observes an exogenous inner value pf (random walk) and the last traded price p.

  • Fundamental agent k possesses a risk premium γk.

  • The order volume depends on abs(pf – p). Higher differences lead to higher order volumes.

  • One buy and one sell order per fundamental agent k and per trading day are generated with: Limit pf - γk for the buy order

  • Limit pf + γk for the sell order


Trend agents l.jpg
Trend Agents

  • Trend agent k observes at time t the prices pt-xkto pt-1

  • Every trading day, trend agent k computes a daily moving average mk of xk days of price p.

  • If p > mk a buy order and if p < mk a sell order is generated at time t with: Limit pt-1 ± μ

  • μis a small random number that is positive if there has been more buy orders than sell orders for pt-1 (G=Geld) and vice-versa (B=Brief).

  • The order volume depends on abs(py– p). py is the price when a switch from buy to sell or vice-versa occurred. Higher differences lead to higher order volumes.


Retail agents 1 l.jpg
Retail Agents (1)

  • Retail Agents are initially not endowed with a trading strategy

  • They are able to adopt both trading strategies (trend, fundamental)

  • They are initially inactive and get activated by an individual price increase at the stock exchange

  • Once activated retail agents may adopt a trading strategy only from their direct neighbors within the communication network. Three cases are possible:

    • no neighbor with strategy no trading, wait

    • neighbor has strategy adopt and start trading

    • several neighbors with strategy adopt the best one and start trading


Retail agents 2 l.jpg
Retail Agents (2)

  • Retail agents stop trading and go into hibernation if an individual price decrease at the stock exchange occurred (e.g. 10%)

  • They sell all their shares over a defined period (e.g. 10 days) and remain inactive for an individual number of days (e.g. 90 days)

  • When the hibernation period is over, they may get activated again depending on their individual threshold


Distribution of agent types l.jpg
Distribution of Agent Types

Green=Retail Agents, Yellow=Fundamental Agents, Red=Trend Agents


Double auction batch limit order book l.jpg
Double Auction Batch Limit Order Book

Orders

Possible Trade Volume

The maximum possible trade volume defines the new price at 1019.



Random network l.jpg
Random Network

Red=Retail Agents, Yellow=Fundamental Agents, Blue=Trend Agents


Small world network l.jpg
Small-World Network

Red=Retail Agents, Blue=Fundamental Agents, Yellow=Trend Agents


Scale free network l.jpg
Scale-Free Network

Red=Retail Agents, Yellow=Fundamental Agents, Blue=Trend Agents


Degree centralization l.jpg
Degree Centralization

The degree centralization measures the variation of the degree of a network member in relation to all other network members. (g=number of nodes, n*=node with highest degree)

The degree centralization varies between 0 and 1. The star network has a degree-centralization of 1.

Freeman, L. C. (1978/79). "Centrality in Social Networks. Conceptual Clarification." Social Networks 1, p. 215-239.


Betweeness centralization l.jpg
Betweeness Centralization

Interactions between two nonadjacent nodes A and B depend on other nodes that exist on the path from node A to node B. The betweenness centralization measures the frequency of a node appearing on the path between the two nonadjacent nodes in relation to the other nodes of the network.

pjk(i) equals the probability that node i is on the path between node j and k

sjk equals the amount of shortest paths between nodes j and k.

The betweenness centralization varies between 0 and 1, it reaches a maximum if a node is on all shortest paths between all other nodes (star network).

Freeman, L. C. (1978/79). "Centrality in Social Networks. Conceptual Clarification." Social Networks 1, p. 215-239.


Closeness centralization l.jpg
Closeness Centralization

The closeness centralization measures how close a node is to the other nodes of a network in relation to the other nodes of the network. It shows how quickly (shortest paths to other nodes) one node can be reached from other nodes.

d(i, j) being the distance (length of the shortest path) between

node i and j.

Freeman, L. C. (1978/79). "Centrality in Social Networks. Conceptual Clarification." Social Networks 1, p. 215-239.





Simulation run with the small world network l.jpg
Simulation Run withthe Small-World Network


Simulation run with the small world network24 l.jpg
Simulation Run with theSmall-World Network


Number of agent types with the small world network l.jpg
Number of Agent Types with the Small-World Network


Slide26 l.jpg

DescriptiveStatistics(10 Runs per Network)


Slide27 l.jpg

Unit-Root andFatTail Properties(10 Runs per Network)


Definition of volatility and distortion l.jpg
Definition of Volatility and Distortion

T = trading days (3,000), P = Price, Pf = inner value

Westerhoff, F. (2003). "Heterogeneous Traders and the Tobin tax." Journal of Evolutionary Economics 13, p. 53-70.





Agent type performance 10 runs per network l.jpg
Agent Type Performance(10 Runs per Network)


Outlook l.jpg
Outlook

  • A cooperation with the Sparkasse Gifhorn-Wolfsburg is in preparation, to find more empirical evidence about the behavior of retail investors.

  • The model parameters are analyzed for their sensitivity and if some may be endogenous.

  • An analytical solution of the simulation model is still needed.

  • Dynamic communication networks might be an interesting extension.


ad