CHENG, Shih-Fen

LIM, Yee Pin

LIU, Chao-Chi

Institutional Knowledge at Singapore Management University

Singapore Management UniversityInstitutional Knowledge at Singapore Management UniversityResearch Collection School Of Information Systems School of Information Systems5-2009An Agent-based Commodity Trading SimulationShih-Fen CHENGSingapore Management University, sfcheng@smu.edu.sgYee Pin LIMChao-Chi LIUFollow this and additional works at: https://ink.library.smu.edu.sg/sis_researchPart of the Artificial Intelligence and Robotics Commons, Business Commons, and theOperations Research, Systems Engineering and Industrial Engineering CommonsThis Conference Proceeding Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge atSingapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorizedadministrator of Institutional Knowledge at Singapore Management University. For more information, please email libIR@smu.edu.sg.CitationCHENG, Shih-Fen; LIM, Yee Pin; and LIU, Chao-Chi. An Agent-based Commodity Trading Simulation. (2009). AAMAS '09:Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems: Budapest, May 10-15. 2, 1377-1378.Research Collection School Of Information Systems.Available at: https://ink.library.smu.edu.sg/sis_research/839An Agent-Based Commodity Trading SimulationShih-Fen ChengSchool of Information SystemsSingapore ManagementUniversitysfcheng@smu.edu.sgYee Pin LimSchool of Information SystemsSingapore ManagementUniversitysharonlim@smu.edu.sgChao-Chih LiuSchool of Information SystemsSingapore ManagementUniversityccliu@smu.edu.sgCategories and Subject DescriptorsI.2.11 [Artificial Intelligence]: Distributed Artificial In-telligence —Multiagent systems; J.4 [Computer Applica-tions]: Social and Behavioral Sciences—EconomicsGeneral TermsEconomics, ExperimentationKeywordsComputational Economics, Commodity Trading1. INTRODUCTIONIn recent years, the study of trading in electronic marketshas received significant amount of attention, particularly inthe areas of artificial intelligence and electronic commerce.With increasingly sophisticated technologies being appliedin analyzing information and making decisions, fully au-tonomous software agents are expected to take up significantroles in many important fields. This trend is most obviousin the financial domain, where speed of reaction is highlyvalued and significant investments have been made in infor-mation and communication technologies.Despite the successes of automated trading in many im-portant classes of financial markets, commodity trading haslagged behind, mainly because of its complicated productcategorization and logistical fulfillment considerations. Thesetwo factors greatly hinder automation efforts because when-ever an event that has significant physical impact on thecommodity supply chain occurs, complicated and commodity-specific reactions (might include trading, re-hedging, or evenlogistic adjustment, to name just a few) would be required.Due to this reason, to master even just a particular com-modity market would take several years of intensive train-ing and exposure. To facilitate better understanding on theevent-centric commodity market, we built an agent-basedcommodity trading simulation that is driven by physicalevents [1]. The simulation platform serves two purposes:First, it is used as a tool that allows more effective train-ing; second, professional trader’s behaviors in face of uncer-tain events could be measured comprehensively for thoroughanalysis.Cite as: An Agent-Based Commodity Trading Simulation, Shih-FenCheng, Yee Pin Lim and Chao-Chih Liu, Proc. of 8th Int. Conf.on Autonomous Agents and Multiagent Systems (AAMAS2009), Decker, Sichman, Sierra and Castelfranchi (eds.), May, 10–15,2009, Budapest, Hungary, pp. XXX-XXX.Copyright c© 2009, International Foundation for Autonomous Agents andMultiagent Systems (www.ifaamas.org). All rights reserved.2. THE COMMODITY MARKETIn our demonstration, commodity trading refers to thetrading of raw or primary products that can be specified bystandard contracts. Examples of such commodities includecrops, oil, or live cattle. Similar to other financial market,a wide variety of derivatives are available for hedging orspeculative purposes. What makes trading in commoditymarkets unique and challenging, despite their similarities tostock and bond markets, are the physical transactions thatare behind all the financial trades in any form of commod-ity market. Although the volume in the financial trades(commodity derivatives) has already overtaken the physicaltrades, physical transactions are still critically important.This is because the balance of supply and demand and theresulting spot prices in the physical transactions are still thefundamental forces that are behind the commodity market,and no matter how sophisticated the used financial instru-ments are, all of them still need to closely reference thesespot prices. Which is why trading in commodity market ischallenging: physical transactions are affected by not onlysupply and demand of the commodity, but also all the physi-cal elements that link together the supply and demand sides.Although the price dynamics of commodity market havebeen studied extensively in the finance domain, to the best ofour knowledge, none of them specifically considers physicalevents as the primary price driver (and thus the possibil-ity of using sequence of events in generating desired marketdynamics). This is how our event-based simulation modeldiffers from most past models.For simplicity, we assume that only one type of commodityfuture is considered and the trades are facilitated by a con-tinuous double auction (CDA). The trading infrastructureis provided by a generic market game server, AB3D [4]. Allevents are revealed sequentially to both human and softwareagents at predetermined times. The primary attributes of anevent are title, content, time to be announced, time windowin which it is effective and its impacts on both buy-side andsell-side strengths. The scenario designer thus has full con-trol over the sequence of events he would like to introduce.This allows rare but important scenarios to be simulatedand experienced (e.g., the recent commodity boom and thesubsequent market crash).The price dynamics in response to the announced eventsare not determined by a fixed econometric model; instead,the price dynamics are generated through the interactionof a set of autonomous agents that play different roles ina commodity chain (typical such roles include producers,consumers, and speculators). This agent-based frameworkallows us to tap into the rich literature of“Agent-based Com-putational Economics” [6]. Individual agents are built withclassical models and are described next.3. AN AGENT-BASE SIMULATIONIn our event-based commodity trading simulation, the pricedynamics of the market is generated by the collective actionsof producers and consumers in response to the sequence ofevents. For simplicity, we assume that producers only tradeshort and consumers only trade long (in other words, pro-ducers and consumers are assumed to have stable flow ofproductions and usages, and they adopt the simple hedge-and-forget strategy). With this assumption, an agent’s de-cision is reduced to the quantity and the price of the bid.Regardless of the type of the trade, the quantity of thebid is assumed to be uniformly distributed within an agent-specific range, which corresponds to the bound on this agent’sproduction or usage. On the other hand, the decision on thebid price is mainly determined by agent’s individually main-tained price prediction. A popular model for the commod-ity price is the mean-reversion process called the Ornstein-Uhlenbeck (OU) process. In this model, the commodityprice prediction is generated by considering both long-termequilibrium price, which is mean-reverting, and event im-pacts. Both these two terms are agent-specific and highlydependent on agent’s role and private information. Con-sidering that perfect prediction is usually not possible in ahighly dynamic market, we assume that the submitted pricewill be randomly chosen within the range of the market priceand the predicted price.While “mean-reverting” producers and consumers consti-tute the “fundamental” part of the simulated market, themarket volatility, on the other hand, is generated by thespeculator agents. In our simulation, we adopt a variant ofthe classical zero intelligence (ZI) strategy [3]. In our simu-lation, ZI agents are allowed to take both long and short po-sitions with equal chance, and their individual trading limitsare the same as human traders. ZI agents must return tozero position at the end of the game.The above bidding models are rigorously defined and im-plemented in [2].4. VALIDATING MARKET DYNAMICSIn our simulation, completely fictitious scenarios could begenerated. Unfortunately, despite the benefits brought byour approach, it also introduces challenges in simulation val-idations. The critical issue here is the lack of real-world datafor the validation purpose and thus we will have to validateour simulation by using the simulation itself. The techniquewe applies here is a statistical method called event studies.A variant of the event study method by MacKinlay [5] isadopted and our initial study demonstrates that we couldindeed generate market dynamics as hinted by the event se-quence (details are provided in [2]).5. TRADING DIAGNOSISThe trader’s interface for our simulation is provided inFigure 1. As stated earlier, besides just building tradingsimulation, another important purpose of our platform isto study people’s trading behaviors in response to variousevents. To facilitate such analysis, whenever a trader per-forms certain action in our trading environment, be it read-Figure 1: Human trader’s interface.ing event description or issuing trades, it will be recorded.In conjunction with this trader’s performance, we could in-fer what trigger’s trader’s trading decisions, and we can testwhether a trader’s performance is related to any particularbehavioral pattern.6. DEMONSTRATION DETAILSThe authors will explain how agents respond to events be-hind the scene. In particular, we would highlight the distinctbehavior by producers, customer, and various types of spec-ulators. We will shed light on how these individual agentscould generate a rich set of price dynamics. Participantsare encouraged to play the role of commodity traders, andexperience some rarely seen extreme scenarios, e.g., marketcrash or commodity boom.Participants will also be able to see their trading diag-noses. These reports are generated after the trading session,and could be used in reviewing one’s trading strategies.7. REFERENCES[1] S.-F. Cheng. Designing the market game for acommodity trading simulation. In IEEE/WIC/ACMInternational Conference on Intelligent AgentTechnology, pages 445–449, San Jose, CA, USA, 2007.[2] S.-F. Cheng and Y. P. Lim. Designing and validating anagent-based commodity trading simulation. Technicalreport, Singapore Management University, 2009.[3] D. K. Gode and S. Sunder. Allocative efficiency ofmarkets with zero-intelligence traders: Market as apartial substitute for individual rationality. TheJournal of Political Economy, 101(1):119–137, 1993.[4] K. M. Lochner, S.-F. Cheng, and M. P. Wellman.AB3D: A market game platform based on flexiblespecification of auction mechanisms. 2007.[5] A. C. MacKinlay. Event studies in economics andfinance. Journal of Economic Literature, 35(1):13–39,March 1997.[6] L. Tesfatsion. Agent-based computational economics:Growing economies from the bottom up. Artificial Life,8(1):55–82, 2002.

An Agent-based Commodity Trading Simulation

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An Agent-based Commodity Trading Simulation

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