Wolf Ketter's Publications

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Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

Wolfgang Ketter, John Collins, Maria Gini, Alok Gupta, and Paul Schrater. Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges. Technical Report 1566-5283, RSM Erasmus University, 2007.

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Abstract

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.

@TechReport{Ketter-ERIM-WP07,
  author =       "Wolfgang Ketter and John Collins and Maria Gini
                  and Alok Gupta and Paul Schrater",
  title =        "Detecting and Forecasting Economic Regimes
                  in Multi-Agent Automated Exchanges",
  year =         2007,
  abstract = "We show how an autonomous agent can use
observable market conditions to characterize the microeconomic
situation of the market and predict future market trends.  The
agent can use this information to make both tactical decisions, such
as pricing, and strategic decisions, such as product mix and
production planning.  We develop methods to learn dominant
market conditions, such as over-supply or scarcity, from historical
data using Gaussian mixture models to construct price density
functions. We discuss how this model can be combined with
real-time observable information to identify the current dominant
market condition and to forecast market changes over a planning
horizon. We forecast market changes via both
a Markov correction-prediction process and
an exponential smoother.
Empirical analysis shows that the exponential smoother yields more accurate
predictions for the current and the next day
(supporting tactical decisions),
while the Markov correction-prediction process is
better for longer term predictions (supporting strategic decisions).
Our approach offers more flexibility than
traditional regression based approaches,
since it does not assume a fixed functional
relationship between dependent and independent variables. We
validate our methods by presenting experimental results in a case
study, the Trading Agent Competition for Supply Chain Management.",
  institution =  "RSM Erasmus University",
  number =       "1566-5283",
  address =      "Rotterdam, The Netherlands",
  bib2html_pubtype = {Unrefereed},
  bib2html_rescat = {Trading Agents: Supply-Chain Management},
}

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