Online double auction mechanism for perishable goods

One-sided auctions are used for market clearing in the spot markets for perishable goods because production cost in spot markets is already “sunk.” Moreover, the promptness and simplicity of one-sided auctions are beneficial for trading in perishable goods. However, sellers cannot participate in the price-making process in these auctions. A standard double auction market collects bids from traders and matches the higher bids of buyers and lower bids of sellers to find the most efficient allocation, assuming that the value of unsold items remains unchanged. Nevertheless, in the market for perishable goods, sellers suffer a loss when they fail to sell their goods, because their salvage values are lost when the goods perish. To solve this problem, we investigate the suitable design of an online double auction for perishable goods, where bids arrive dynamically with their time limits. Our market mechanism aims at improving the profitability of traders by reducing trade failures in the face of uncertainty of incoming/departing bids. We develop a heuristic market mechanism with an allocation policy that prioritizes bids of traders based on their time-criticality, and evaluate its performance experimentally using multi-agent simulation. We find out that our market mechanism realizes efficient and fair allocations among traders with approximately truthful behavior in different market situations.     

Exchange market for complex commodities: search for optimal matches

The Internet has led to the development of online markets, and computer scientists have designed various auction algorithms, as well as automated exchanges for standardized commodities; however, they have done little work on exchanges for complex non-standard goods. We present an exchange system for trading complex goods, such as used cars or non-standard financial securities. The system allows traders to represent their buy and sell orders by multiple attributes; for example, a car buyer can specify a model, options, colour, and other desirable features. Traders can also provide complex price constraints, along with preferences among acceptable trades; for instance, a car buyer can specify dependency of an acceptable price on the model, year of production, and mileage. We describe the representation and indexing of orders, and give algorithms for fast identification of matches between buy and sell orders. The system identifies the most preferable matches, which maximize trader satisfaction, and it allows control over the trade-off between speed and optimality of matching. It supports markets with up to 300?000 orders, and processes hundreds of new orders per second.     

Characterising trader manipulation in a limit-order driven market

Use of trading strategies to mislead other market participants, commonly termed trade-based market manipulation, has been identified as a major problem faced by present day stock markets. Although some mathematical models of trade-based market manipulation have been previously developed, this work presents a framework for manipulation in the context of a realistic computational model of a limit-order market. The Maslov limit order market model is extended to introduce manipulators and technical traders. We show that “pump and dump” manipulation is not possible with traditional Maslov (liquidity) traders. The presence of technical traders, however, makes profitable manipulation possible. When exploiting the behaviour of technical traders, manipulators can wait some time after their buying phase before selling, in order to profit. Moreover, if technical traders believe that there is an information asymmetry between buy and sell actions, the manipulator effort required to perform a “pump and dump” is comparatively low, and a manipulator can generate profits even by selling immediately after raising the price.     

Fraud detection in play-money prediction markets

Prediction markets are increasingly used to aggregate information on particular future events of interest such as elections, sports events, and Oscar winners. However, prediction markets are sensitive to manipulation and price distortions. In this paper, we show evidence for fraud in a play-money sports prediction market. In contrast to the often considered outcome manipulation in the context of political stock markets we were only looking for violations of our general terms and conditions, i.e., for traders creating multiple user accounts and trading against themselves in order to transfer cash from one account to another. We found evidence of suchlike coalitions and received complaints about it by annoyed traders. We discuss possible countermeasures in order to avoid or at least detect this kind of fraud in play-money prediction markets and present a tool which can be used to detect coalition building while operating a market.     

A case study on using prediction markets as a rich environment for active learning

In this paper, prediction markets are presented as an innovative pedagogical tool which can be used to create a Rich Environment for Active Learning (REAL). Prediction markets are designed to make forecasts about specific future events by using a market mechanism to aggregate the information held by a large group of traders about that event into a single value. Prediction markets can be used to create decision scenarios which are linked to real-world events. The advantages of this approach in the cognitive and affective domains of learning are examined. The unique ability of prediction markets to enable active learning in large group teaching environments is explored. Building on this theoretical work, a detailed case study is presented describing how a prediction market can be deployed as a pedagogical tool in practice. Empirical evidence is presented exploring the effect prediction market participation has on learners in the cognitive domain.     

A multi‐agent system for analyzing the effect of information on prediction markets

Prediction markets have been shown to be a useful tool for forecasting the outcome of future events by aggregating public opinion about the event's outcome. In this paper, we investigate an important aspect of prediction markets—the effect of different information‐related parameters on the behavior of the traders in the market. We have developed a multi‐agent based system that incorporates different information‐related aspects including the arrival rate of information, the reliability of information, the penetration or accessibility of information among the different traders, and the perception or impact of information by the traders. We have performed extensive simulations of our agent‐based prediction market for analyzing the effect of information‐related parameters on the traders' behaviors expressed through their trading prices, and compared our agents' strategies with another agent‐based pricing strategy used in prediction markets called the zero intelligence strategy. Our results show that information‐related parameters have a significant impact on traders' beliefs about event outcomes, and, frequent, reliable information about events improves the utilities that the traders receive. Overall, our work provides a better understanding of the effect of information on the operation of prediction markets and on the strategies used by the traders in the market. © 2011 Wiley Periodicals, Inc.     

A note on the relationship between market efficiency and adaptability – New evidence from artificial stock markets

We developed various artificial stock markets populated with different numbers of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We then applied the STGP technique to historical data from three indices – the FTSE 100, S&P 500, and Russell 3000 – to investigate the formation of stock market dynamics and market efficiency. We used several econometric techniques to investigate the emergent properties of the stock markets. We have found that the introduction of increased heterogeneity and greater genetic diversity leads to higher market efficiency in terms of the Efficient Market Hypothesis (EMH), demonstrating that market efficiency does not necessarily correlate with rationality assumptions. We have also found that stock market dynamics and nonlinearity are better explained by the evolutionary process associated with the Adaptive Market Hypothesis (AMH), because different trader populations behave as an efficient adaptive system evolving over time. Hence, market efficiency exists simultaneously with the need for adaptive flexibility. Our empirical results, generated by a reduced number of boundedly rational traders in six of the stock markets, for each of the three financial instruments do not support the allocational efficiency of markets, indicating the possible need for governmental or regulatory intervention in stock markets in some circumstances.     

Platform design for large-scale artificial market simulation and preliminary evaluation on the K computer

Artificial market simulations have the potential to be a strong tool for studying rapid and large market fluctuations and designing financial regulations. High-frequency traders, that exchange multiple assets simultaneously within a millisecond, are said to be a cause of rapid and large market fluctuations. For such a large-scale problem, this paper proposes a software or computing platform for large-scale and high-frequency artificial market simulations (Plham: /pl\(\Lambda\)m). The computing platform, Plham, enables modeling financial markets composed of various brands of assets and a large number of agents trading on a short timescale. The design feature of Plham is the separation of artificial market models (simulation models) from their execution (execution models). This allows users to define their simulation models without parallel computing expertise and to choose one of the execution models they need. This computing platform provides a prototype execution model for parallel simulations, which exploits the variety in trading frequency among traders, that is, the fact that some traders do not require up-to-date information of markets changing in millisecond order. We evaluated a prototype implementation on the K computer using up to 256 computing nodes.     

Bubble Formation and Heterogeneity of Traders: A Multi-Agent Perspective

Financial bubble is an intensively discussed but quite controversial topic. In current literature, the researches usually focus on the (ir)rationality of traders and its impacts on the bubble. We thereby propose a completely different perspective, that is, of traders’ heterogeneity and its impacts on the formation of bubble in financial markets. As in the real financial markets, the agents are always heterogenous. For example, some of them are fundamentalists, some are chartists, some are noise traders, etc. To model the heterogeneity of agents in the real markets, we proposed a multi-agent model to control the constitution of traders. Based on four scenarios with different constitution of traders’ behaviors, we investigated three extreme situations where the market is occupied by homogeneous agents (no matter they are fundamentalists, chartists or noise traders), and one scenario where the market is made up of heterogeneous traders. By applying Log-Periodic Power-Law (LPPL) model, We studied the impacts of different investors’ behaviors on the bubble formation in the market and found that: (a) the public information has an important influence on the beginning of a bubble; (b) traders’ different expectations and their self-feedback is one of reasons for the existence of log-periodicity in bubble; (c) the existence of power–law growth and log-periodicity, which leads the probability of prediction for the bursting of bubble, is caused by the combined effects of public information, traders’ different expectations and their self-feedback.     

A fuzzy logic control using a differential evolution algorithm aimed at modelling the financial market dynamics

The topic of modelling financial market price movements is in the heart of a wide ranging debate between fundamentalists and behaviourists. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the behaviour of two categories of traders. While the irrational traders are known by a shift in their sentiments, the rational ones have a limited capacity of arbitration. While taking into account the fuzzy complementarity between the fundamentalists and the behaviourists in the explanation of financial market dynamics, this study investigates the development of a new modelling technique using fuzzy sets optimized through differential evolution. This new technique provides some applicable results in the explanation of the dynamical emergent and international financial markets.     

Human and Artificial Agents in a Crash-Prone Financial Market

We introduce human traders into an agent based financial market simulation prone to bubbles and crashes. We find that human traders earn lower profits overall than do the simulated agents (“robots”) but earn higher profits in the most crash-intensive periods. Inexperienced human traders tend to destabilize the smaller (10 trader) markets, but have little impact on bubbles and crashes in larger (30 trader) markets and when they are more experienced. Humans’ buying and selling choices respond to the payoff gradient in a manner similar to the robot algorithm. Similarly, following losses, humans’ choices shift towards faster selling.     

Interacting oligopolistic and oligopsonistic Cournot markets

This paper considers the model of several interacting Cournot markets. Some of them are final goods markets, while the others are resource markets. The markets interact by sharing the same set of economic agents (producers), so that the latter are oligopsonists on resource markets and simultaneously oligopolists on goods markets. Each producer strategically chooses its supply volumes on each goods market and its purchase volume of resources in accordance with technology and expected supply effects on prices. We prove that in the case of linear demand and supply functions the model of interacting Cournot markets is reduced to a potential game; hence, the Nash equilibrium problem is equivalent to a mathematical programming problem. We also discuss the advantages and special features of such a representation of interacting oligopolistic and oligopsonistic markets.     

An Heterogeneous, Endogenous and Coevolutionary GP-Based Financial Market

Stock markets are very important in modern societies and their behavior has serious implications for a wide spectrum of the world's population. Investors, governing bodies, and society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyzing such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of their results. This motivates alternative methods.In this paper, we report an artificial financial market and its use in studying the behavior of stock markets. This is an endogenous market, with which we model technical, fundamental, and noise traders. Nevertheless, our primary focus is on the technical traders, which are sophisticated genetic programming based agents that co- evolve (by learning based on their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identify the conditions under which the statistical properties of price series in the artificial market resemble some of the properties of real financial markets. By performing a careful exploration of the most important aspects of our simulation model, we determine the way in which the factors of such a model affect the endogenously generated price. Additionally, we model the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets, mainly as a suitable model for economic learning on an agent- based simulation.     

Protecting buying agents in e-marketplaces by direct experience trust modelling

In this paper, we describe a framework for modelling the trustworthiness of sellers in the context of an electronic marketplace where multiple selling agents may offer the same good with different qualities and selling agents may alter the quality of their goods. We consider that there may be dishonest sellers in the market (for example, agents who offer goods with high quality and later offer the same goods with very low quality). In our approach, buying agents use a combination of reinforcement learning and trust modelling to enhance their knowledge about selling agents and hence their opportunities to purchase high value goods in the marketplace. This paper focuses on presenting the theoretical results demonstrating how the modelling of trust can protect buying agents from dishonest selling agents. The results show that our proposed buying agents will not be harmed infinitely by dishonest selling agents and therefore will not incur infinite loss, if they are cautious in setting their penalty factor. We also discuss the value of our particular model for trust, in contrast with related work and conclude with directions for future research.     

Traders' Long-Run Wealth in an Artificial Financial Market

In this paper, we study the long-run wealth distribution of agents with different trading strategies in the framework of the Genoa Artificial Stock Market.The Genoa market is an agent-based simulated market able to reproduce the main stylised facts observed in financial markets, i.e., fat-tailed distribution of returns and volatility clustering. Various populations of traders have been introduced in a`thermal bath' made by random traders who make random buy and sell decisions constrained by the available limited resources and depending on past price volatility. We study both trend following and trend contrarian behaviour; fundamentalist traders (i.e., traders believing that stocks have a fundamental price depending on factors external to the market) are also investigated. Results show that the strategy alone does not allow forecasting which population will prevail. Trading strategies yield different results in different market conditions. Generally, in a closed market (a market with no money creation process), we find that trend followers lose relevance and money to other populations of traders and eventually disappear, whereas in an open market (a market with money inflows), trend followers can survive, but their strategy is less profitable than the strategy of other populations.     

Semantic specification and evaluation of bids in web-based markets

In recent years, electronic markets have gained much attention as institutions to allocate goods and services efficiently between buyers and sellers. By leveraging the Web as a global communication medium, electronic markets provide a platform that allows participants throughout the world to spontaneously exchange products in a flexible manner. However, ensuring interoperability and mutual understanding in such a highly dynamic and heterogenous environment can easily become very tricky, particularly if the services and goods involved are complex and described by multiple attributes. In this paper, we present a comprehensive ontology framework that allows the specification of bids for Web-based markets. By expressing utility function policies with a logic-based and standardized formalism, the framework enables a compact bid representation particularly for highly configurable goods and services while ensuring a high degree of interoperability. To facilitate matchmaking of offers and requests in the market, a method for evaluating bids based on logical reasoning is presented. In addition, as proof of concept, we show how the framework can be applied in a Web service selection scenario.     

The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

Computational experimentations in market and supply-chain co-design: a mixed agent approach

The synthetic environment for analysis and simulations (SEAS) is a computational experimentation environment that mimics real life economies, with multiple interlinked markets, multiple goods and services, multiple firms and channels and multiple consumers, all built from the ground up. It is populated with human agents who make strategically complex decisions and artificial agents who make simple but detail intensive decisions. These agents can be calibrated with real data and allowed to make the same decisions in this synthetic economy as their real life counterparts. The resulting outcomes can be surprisingly accurate. This paper discusses the research in this area and goes on to detail the architecture of SEAS. It also presents a detailed case study of market and supply-chain co-design for business-to-business e-commerce in the PC industry.     

An equilibrium analysis of market selection strategies and fee strategies in competing double auction marketplaces

In this paper, we propose a game-theoretic framework for analysing competing double auction marketplaces that vie for traders and make profits by charging fees. Firstly, we analyse the equilibrium strategies for the traders’ market selection decision for given market fees using evolutionary game theory. Using this approach, we investigate how traders dynamically change their strategies, and thus, which equilibrium, if any, can be reached. In so doing, we show that, when the same type of fees are charged by two marketplaces, it is unlikely that competing marketplaces will continue to co-exist when traders converge to their equilibrium market selection strategies. Eventually, all the traders will congregate in one marketplace. However, when different types of fees are allowed (registration fees and profit fees), competing marketplaces are more likely to co-exist in equilibrium. We also find that sometimes all the traders eventually migrate to the marketplace that charges higher fees. We then further analyse this phenomenon, and specifically analyse how bidding strategies and random exploration of traders affects this migration respectively. Secondly, we analyse the equilibrium strategies of the marketplaces when they have the ability to vary their fees in response to changes in the traders’ market selection strategies. In this case, we consider the competition of the marketplaces as a two-stage game, where the traders’ market selection strategies are conditional on the market fees. In particular, we use a co-evolutionary approach to analyse how competing marketplaces dynamically set fees while taking into account the dynamics of the traders’ market selection strategies. In so doing, we find that two identical marketplaces undercut each other, and they will eventually charge the minimal fee as we set that guarantees positive market profits for them. Furthermore, we extend the co-evolutionary analysis of the marketplaces’ fee strategies to more general cases. Specifically, we analyse how an initially disadvantaged marketplace with an adaptive fee strategy can outperform an initially advantaged one with a fixed fee strategy, or even one with an adaptive fee strategy, and how competing marketplaces evolve their fee strategies when different types of fees are allowed.     

The effect of malicious manipulations on prediction market accuracy

Prediction markets are a form of group decision support system which uses a market mechanism to elicit and aggregate information from large numbers of individuals. The literature recognises their potential as decision support tools, but also notes several issues of concern regarding their utility in an organisational setting. One critical concern is the possibility that prediction markets may be subject to malicious manipulation. This paper presents a field experiment which examines the effect of such manipulations on prediction market performance. We divide a sample of 72 contracts into a control group and an experimental group. Contracts in the experimental group are manipulated by a trader with a malicious motivation. The study demonstrates that manipulations do have an effect on prediction market accuracy, but that these effects are rapidly ameliorated by rational traders and shows that fear of malicious manipulation should not preclude the use of prediction markets as organisational decision making tools.