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|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|16528||2011||9 صفحه PDF||سفارش دهید||7118 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 12, November–December 2011, Pages 15134–15142
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.
In the last two decades, internet advent has facilitated the emergence of electronic trading, gathering a lot of interest from both academic and professional organisms. The ultimate purposes were (a) creating stable and efficient trading platforms running on the internet and (b) designing autonomous agents able to trade on behalf of human buyers and sellers (Tesfatsion, 2003), the agents should be able to take decisions from market variables like S&D,1 price history, etc. Furthermore, the generated price pattern should reflect the underlying S&D situation. Among the earliest experiments, the Santa Fe simulator was a typical computer model of the stock market allowing to carry out simulations and tests the effects of different trading scenarios on the price behavior (LeBaron et al., 1999 and Palmer et al., 1994), leading the way to agent-based technology entering the arena of electronic trading. A genetic approach developed by Arthur, Holland, LeBaron, Palmer, and Tayler (1997) helped to clarify the links between fundamental and technical trading, this explained partially how bubbles and financial crushes occur (Levy, 2008 and Roll, 1988). Automated trading had met success in several fields (Kearns & Ortiz, 2003), tough in the case of commodities the progress was hampered by some considerations, mainly logistical features and product characterizations (Arunachalam & Sadeh, 2005). An automated agent is a software program which acts on behalf of its designer or owner to satisfy his/her interests. The owner delegates to his agent the authority to search opportunities and transact with other agents on his behalf. Preist (1999) has designed an agent-based technic for trading commodities via the Internet: the participants dictate to their automated agents rules like “if the price is $ x then buy or sell y units”. Agent-based were also used to evaluate the performance of trading strategies in heterogeneous populations of traders ( Cai, Niu, & Parsons, 2008) and analyzing linkages between price and volumes ( Chen & Liao, 2005). Automated traders with limited intelligence were tested by Gode and Sunder (1993), their setup achieved market price equilibrium. Shelton (1997) described an interesting trading strategy for the futures market in the context of stochastic games against nature. The futures market is a major part of nowadays commercial exchanges, it is the place where futures contracts are traded. A futures contract is a binding agreement between a seller and a buyer, it is related to a specific commodity, 2 like crude oil, gold, metals, grains, oilseeds, etc. A typical feature of a futures transaction is that the price of the commodity is fixed at the present time, whereas the effective delivery of the merchandize, from the seller to the buyer, will occur at a future date, which could be several months or years later. The majority of raw commodities’ producers, processors, consumers, and merchants buy and/or sell futures contracts in order to hedge their price risk, i.e. protect themselves against unforseen sharp price variations ( Hull, 2002 and Teweles and Jones, 1999). So far, automated trading in the futures market was limited to computerizing exchanges’ platforms which once were operated by human pit brokers receiving orders, by telephone or other means, from external human traders, then proceed to their execution in an open outcry market. This first-step automatization process has met a great success with the advent of electronic platforms, consequently pit brokerage is progressively disappearing (Barcley et al., 2003 and Weber, 2006). However, human traders, representing the interest of commercial companies (producers, farmers, refiners, consumers, etc.) are still operating. They constantly asses the market S&D balance as well as with their specific needs, then translate their judgments into sale or buy orders (CBOT, 1998). Our investigation is an attempt to expand the automatization process into a new border by eliminating human traders in the decision making process, and replace them by artificial agents who analyze the market fundamentals (supply and demand), then issue sale and buy orders to the exchange platform. Arunachalam and Sadeh (2005) enumerated the difficulties in automatizing commodities, markets. In earlier works, like Preist (1999), the decision process was directly supervised by human traders. In other setups (Cheng, 2008), only one automated trader is playing the market game with other human traders. In our setup, all traders are automatons, and the decision process is thoroughly in their hands. Furthermore, in contrast to several works where the purpose was maximizing the profit of participants, our work is rather focusing on market stability, that is designing a market institution with less crushes and bubbles. Hereafter,3 we create autonomous trading agents able to negotiate the price of futures contracts. The trading agents will be equipped with trading strategies reacting to S&D forecasts and other market data in order to generate sale and purchase orders. In turn, the interactions of these market orders will generate a price curve over the trading horizon. To measure the performance of the trading strategies, we propose to measure the distance between the generated price curve (or market price curve) compared to a benchmark price curve. For this reason, a discussion over the price’s role in the market is necessary to establish some hypotheses on the properties of the benchmark price curve. Then we formulated the corresponding mathematical criteria allowing to measure the actual distance between the benchmark and market price curves. To show how this can work in practice, we suggested a framework of an artificial futures market composed from a seller agent and a buyer agent representing the interests of a producer and a consumer respectively. The agents are fed with a regular stream of forecasts on S&D levels over a trading horizon of m periods. The agents react first by adjusting their forecasts then they issue sale and purchase orders. To run the model, we suggested a parameterized trading strategy based upon the gap between S&D levels and price bands built around a nominal price. Finally, we used simulation to search for optimal parameters of the trading strategies by maximizing an aggregate performance function. The next section is a discussion over the price’s role in the market and its important link to the S&D balance. This will lead to formulating some hypotheses on the benchmark price curve, then deriving analytical measures to evaluate the performance of a given trading strategy. The third section describes the setup of the futures market adapted to the producer–consumer case. The forth section provides an example of a trading strategy for the autonomous trading agents. Illustrative tabular and graphical results are provided in the last section.
نتیجه گیری انگلیسی
We suggested herein a technical approach allowing to operate an artificial futures market with automated traders. The proposed producer–consumer setup retains the most essential features of real futures markets: it allows fixation of price ahead of the effective delivery of the merchandize, more importantly this framework assumes uncertainty in the levels of S&D and aggregates their updates easily. On the other hand, the obtained numerical results were appealing: the futures price follows closely the prevailing S&D balance and the underlying nominal price. Automated trading remains an open research field necessitating new contributions in several directions. Looking forward, our approach needs to be generalized to the case of many producers and consumers, and even speculators should be involved to create more liquidity. The ultimate objective should be designing an efficient futures market institution operated entirely by automatons. In this optic, the main hypotheses of the benchmark price curve need to be enriched by taking into account other market subtleties. In addition, the parametrization can be conducted in a different manner. For instance, it would be interesting to see what happens if the requested quantities u12 and u22 were parameterized too. However, one should keep in mind that the number of parameters should be kept to a minimum in order to obtain an optimal solution in a reasonable time. Simulation can be bypassed by establishing the underlying mathematical model of the approach presented herein, then searching the optimal solution using numerical methods.