A computational approach to modeling commodity markets

Expert Systems with Applications, 2011

Automatizing commodities' price negotiation was hard to achieve in practice, mainly because of logistical complications. The purpose of our work is to show that it is possible to automatize thoroughly commodities' trading in the futures market by replacing human traders with artificial agents. As a starting step, we designed a market institution, called producer-consumer, where only an automated seller and an automated buyer can trade on behalf of the producer and consumer, respectively. The producer and consumer periodically feed their trading agents with supply and demand (S&D) forecasts. We suggested a parameterizable trading strategy, called bands and frequencies, for the agents. To measure the overall efficiency of this trading system in terms of price stability and liquidity, we made some hypotheses on the benchmark price curve and its linkages to S&D curves and other relevant market variables. Then we proposed analytical tools to measure strategy performance. Finally, we conducted some computer simulations to prove the workability of this approach.

2009

In this paper, an agent-based commodity trading simulation platform is presented, with focus on simulation design and validation. We propose a novel combination of event-based approach and event study method for market dynamics generation and validation. In our event-based approach, the simulation is progressed by announcing news events that affect various aspects of the commodity supply chain. Upon receiving these events, market agents that play different roles, e.g., producers, consumers, and speculators, would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively the market dynamics will be shaped. The generated market dynamics can then be validated by a variant of the event study method. We demonstrate how the methodology works with several numerical experiments and conclude by highlighting the practical significance of such simulation platform.

Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, 2009

In this paper, an event-centric commodity trading sim- ulation powered by the multiagent framework is pre- sented. The purpose of this simulation platform is for training novice traders. The simulation is progressed by announcing news events that affect various aspects of the commodity supply chain. Upon receiving these events, market agents that play the roles of producers, consumers, and speculators

This paper presents an agents based modeling and simulation of Indonesian rice price system. I view Indonesian rice prices as individual subjective estimates of payoff-maximizing exchange ratios, and hence are as private information of individual agents who are trading in Indonesian rice market. In our model, those agents i.e., farmers, small rice-stock traders (small traders), big rice-stock traders (big traders), and BULOG (Indonesian logistics national company) presumably produce, exchange, and consume two goods: imported rice and national rice. We treat this system as complex, dynamic, nonlinear system in which agents have limited information. The system evolves rapidly from an initial random seeding of private prices towards what may be termed a private but common price structure. This model provides a general, decentralized disequilibrium adjustment mechanism that renders market equilibrium dynamically stable in a highly simplified production and exchange economy. Through this study, I seek to find and analyze patterns and determining policies that will help the rice prices moves through time towards the market clearing price system for the underlying general equilibrium system, thus our economy may enjoy rice price stability.

Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics, 2000

This is an introductory work to the field of automatizing futures markets, related to commodities, so far operated by human traders. First, we build a mathematical framework for a futures market with many producers and consumers represented by automated traders in the market platform. Then we suggest an automatic trading strategy for the automatons. This strategy takes into account the forecasts of supply and demand streams as well as the evolution of nominal price. Later, we recall a set of analytical criteria used to measure the performance of a trading strategy. Next, we illustrate our approach by showing a price pattern generated by the automatic strategy and calculate its performances. Finally, we exhibit a heuristic based on simulation allowing to compute a quasi-optimal parameters matrix for this automatic trading system.

European Journal of Operational Research, 2005

auction that enables the implementation of the direct optimized market and approximates the behaviour of the "ideal" direct optimized mechanism. The process allows buyers and sellers to modify their initial bids, including the technological constraints. The proposed market designs are particularly relevant for industries related to natural resources. We present the models and algorithms required to implement the optimized market mechanisms, describe the operations of the multi-round auction, and discuss applications and perspectives.

This is an introductory work to trade automatization of the futures market, so far operated by human traders. We are not focusing on maximizing individual profits of any trader as done in many studies, but rather we try to build a stable electronic trading system allowing to obtain a fair price, based on supply and demand dynamics, in order to avoid speculative bubbles and crashes. In our setup, producers and consumers release regularly their forecasts of output and consumption respectively. Automated traders will use this information to negotiate price of the underlying commodity. We suggested a set of analytical criteria allowing to measure the efficiency of the automatic trading strategy in respect to market stability.

:N ew kinds of markets demand,ne wk inds of market design. In the past twenty years several ne wk inds of market have been devised and put into operation, sometimes after several false starts. Designing markets is a ne wa ctivity for economists, who haven’ tr eadily thought of themselves as engineers. Emergence of the market engineer has been hastened by the interest of computer scientists in designing on-line markets, and there has been a three-way marriage of game theory ,e xperimental results, and computer science in the use of computer simulation models to analyse, and to design, ne wm,arkets. This paper argues for several things. Fo rs imulation as an alternative to closed-form analysis in market analysis and design. Fo ra gent-based computational economic models as specific simulation models. Fo re xplicit validation of such models, using several heuristics. The paper discusses recent research into market design and simulation. 1. Email: bobm@agsm.edu.au,.A ne arlier version o...

2007

In this paper, we propose to design a market game that (a) can be used in modeling and studying commodity trading scenarios, and (b) can be used in capturing human traders' behaviors. Specifically, we demonstrate the usefulness of this commodity trading game in a single-commodity futures trading scenario. A pilot experiment was run with a mixture of human traders and an autonomous agent that emulates the aggregated market condition, with the assumption that this autonomous agent would hint each of its action through a public announcement. We show that the information collected from this simulation can be used to extract the pattern of successful human traders. Finally, we elaborate on the potential of this market game in studying autonomous commodity trading.

Handbook of Computational Economics, 2006

This chapter explores the state of the emerging practice of designing markets by the use of agent-based modeling, with special reference to electricity markets and computerized (on-line) markets, perhaps including real-life electronic agents as well as human traders. The paper first reviews the use of evolutionary and agent-based techniques of analyzing market behaviors and market mechanisms, and economic models of learning, comparing genetic algorithms with reinforcement learning. Ideal design would be direct optimization of an objective function, but in practice the complexity of markets and traders' behavior prevents this, except in special circumstances. Instead, iterative analysis, subject to design criteria trade-offs, using autonomous self-interested agents, mimics the bottom-up evolution of historical market mechanisms by trial and error. The chapter highlights ten papers that exemplify recent progress in agent-based evolutionary analysis and design of markets in silico, using electricity markets and on-line double auctions as illustrations. A monopoly sealed-bid auction is examined in the tenth paper, and a new auction mechanism is evolved and analyzed. The chapter concludes that, as modeling the learning and behavior of traders improves, and as the software and hardware available for modeling and analysis improves, the techniques will provide ever greater insights into improving the designs of existing markets, and facilitating the design of new markets.