Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Kommunikations-Netzwerk-Topologie und Marktverhalten 15. Februar 2008 von Oliver Hein Forschungskolloquium 2008

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 1.Frankfurt Artificial Stock Market -Components -Agent Types -Auction Method 2.Networks -Network Topologies -Network Centralization Measures 3.Simulation -Parameters -Simulation Results: Centralization against Volatility and Distortion Agent Type Performance 4.Outlook Contents

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Frankfurt Artificial Stock Market The Frankfurt Artificial Stock Market (FASM) 1.6 is available for download at: But there is no documentation yet! Only articles exist that describe the system.

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Frankfurt Artificial Stock Market (FASM) ver. 1.6

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Handelsablauf

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Kommunikationsnetzwerke Börse Meistausführungsprinzip Blau=kaufen, Rot=verkaufen Scale-Free-NetzwerkZufallsnetzwerk

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Fundamental Agents Fundamental agent k observes an exogenous inner value p f (random walk) and the last traded price p. Fundamental agent k possesses a risk premium γ k. The order volume depends on abs(p f – 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 p f - γ k for the buy order Limit p f + γ k for the sell order

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Trend Agents Trend agent k observes at time t the prices p t-x k to p t-1 Every trading day, trend agent k computes a daily moving average m k of x k days of price p. If p > m k a buy order and if p < m k a sell order is generated at time t with: Limit p t-1 ± μ μ is a small random number that is positive if there has been more buy orders than sell orders for p t-1 (G=Geld) and vice-versa (B=Brief). The order volume depends on abs(p y – p). p y is the price when a switch from buy to sell or vice-versa occurred. Higher differences lead to higher order volumes.

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 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: 1.no neighbor with strategy no trading, wait 2.neighbor has strategy adopt and start trading 3.several neighbors with strategy adopt the best one and start trading

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 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 Retail Agents (2)

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Distribution of Agent Types Green=Retail Agents, Yellow=Fundamental Agents, Red=Trend Agents

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Double Auction Batch Limit Order Book The maximum possible trade volume defines the new price at OrdersPossible Trade Volume

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) NETWORKS

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Random Network Red=Retail Agents, Yellow=Fundamental Agents, Blue=Trend Agents

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Small-World Network Red=Retail Agents, Blue=Fundamental Agents, Yellow=Trend Agents

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Scale-Free Network Red=Retail Agents, Yellow=Fundamental Agents, Blue=Trend Agents

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 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

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 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. 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). s jk equals the amount of shortest paths between nodes j and k. p jk (i) equals the probability that node i is on the path between node j and k Freeman, L. C. (1978/79). "Centrality in Social Networks. Conceptual Clarification." Social Networks 1, p

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 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

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Used Centralization Measures Network Types Betweenness Centralization Closeness Centralization Degree Centralization Random , Small-World Scale-Free Scale-Free

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) SIMULATIONS AND RESULTS

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Simulation Parameters General agents’ parameters agent type#τtime windowinitial cashinitial shares fundamental290.5% - 3.5%−5 - 8 mil.5, , 000 trend18− days1 - 2 mil.2, , 000 retail453−−1 – 1.5 mil.0 Retail agents’ specific parameters activation threshold de-activation threshold hibernationprofit windowsell period 5% - 10%10% - 18% days days10 days Order volumes of trend agentsOrder volumes of fundamental agents deviation from signal order volume in shares deviation from signal order volume in shares 0% − 2%2 1 2% − 5%52% − 4%3 5% − 10%154% − 7%5 10% − ∞807% − ∞20

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Simulation Run with the Small-World Network

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Simulation Run with the Small-World Network

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Number of Agent Types with the Small-World Network

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Descriptive Statistics (10 Runs per Network) A. Descriptive Statistics of Daily Log-Yields RandomSmall-WorldScale-Free 1Scale-Free 2 Mean0% Standard Deviation0.74%0.78%0.83%0.91% Skewness Min.-5.32%-7.45%-8.48%-13.22% Max.5.63%5.26%5.60%7.35%

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Unit-Root and Fat Tail Properties (10 Runs per Network) B. Unit-Root RandomSmall-WorldScale-Free 1Scale-Free 2 Augmented-Dickey-Fuller (ADF) Test: 1% level: % level: % level:-2.56 ADF C. Fat Tail Property RandomSmall-WorldScale-Free 1Scale-Free 2 Kurtosis Hill-Estimator (5% tail)

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Definition of Volatility and Distortion T = trading days (3,000), P = Price, P f = inner value Westerhoff, F. (2003). "Heterogeneous Traders and the Tobin tax." Journal of Evolutionary Economics 13, p

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Volatility and Agent Type Performance D. Volatility, Distortion and Volume (average for 10 runs) RandomSmall-WorldScale-Free 1Scale-Free 2 Volatility Distortion Volume (shares)443,375568,299691,6991,133,726 E. Agent Type Performance (average for 10 runs) RandomSmall-WorldScale-Free 1Scale-Free 2 Fundamental10.53%13.57%17.14%34.91% Trend-11.39%-15.03%-18.93%-19.12% Retail-6.60%-8.07%-11.36%-22.65%

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Volatility and Network Centralization (10 Runs per Network)

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Distortion and Network Centralization (10 Runs per Network)

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) Agent Type Performance (10 Runs per Network)

Oliver Hein, Goethe University, Frankfurt, Financial Agent-based Computational Economics (FINACE) 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.