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Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies.

In this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other using t-tests and kernel density plots.

The results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy.

This paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.

Purpose – This study constitutes a first attempt to experimentally test the performance of a 100% auction versus a 100% free allocation of CO2 permits under the rules and parameters that mimic the EU ETS (imperfect competition, uncertainty in emissions' control, and allowing banking), with environmental targets more restrictive than the current ones but foreseeable for the near future.

Methodology/approach – Two experimental treatments were run to achieve our goal. Both included the rules and the parameters that parallel the EU ETS structure, the only difference being the rule for the primary allocation of permits.

Findings – Our experimental results indicate that the EU ETS has the potential to reduce CO2 emissions, achieving targets considerably more restrictive than the current ones at high efficiency levels, both with auctioned and free emission permits.

Practical implications – Concerns about undue scarcity, and corresponding high prices, in secondary markets generated by a primary auction market are not warranted under the proposed dynamic auction format. This adds arguments favoring auctioning over grandfathering as the rule for the initial allocation of emission permits in the EU ETS.

Originality/value of chapter – This study implements a theoretically appropriate auction format for the primary allocation of emission permits (the Ausubel (2004) auction) and incorporates a first attempt to include in the analysis measures of the risk preferences of subjects participating in emission permits experiments. These characteristics are for the first time implemented under a complex experimental design (including uncertainty of emission abatement, and banking), trying to parallel the EU ETS trading environment.

An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes.

A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests.

In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders.

Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult.

The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.

Systemic risk propagated through over‐the‐counter (OTC) derivatives can best be managed by a public‐private central counterparty clearing house. The purpose of this paper is to outline the market microstructure necessary for such a clearing house.

The paper proposes using an request for quote platform with an active permissioning system that uses analytic approximations based on Monte Carlo simulation to estimate default risk and a two‐part pricing scheme to efficiently price that risk.

It is found that comprehensive clearing for complex and standardized derivatives is feasible using the clearing framework.

This research is limited by the authors' ability to give empirical examples. The paper gives a short example with data, but given the constraints on length, cannot go into more detail.

This comprehensive clearing structure, in contrast to current proposed government regulations, will not drive out the “good” with the “bad” OTC derivative instruments.

This is the only paper the authors are aware of that outlines a detailed framework for clearing all OTC derivatives.

In this paper, we study the effect of induced positive and negative moods on traders' willingness to trade (pay and accept) in experimental asset markets.

We conduct experimental asset markets where subjects undergo a mood induction procedure prior to trade. After the subjects are induced with either negative or positive affect, they can trade an experimental asset with a known stream of dividends for a known number of periods.

We first show that both positive and negative affects are associated with larger positive deviations from fundamental values. We also show that when subjects are induced with positive mood, they bid higher prices but for fewer units of the stock. On the supply side, positive affect is associated with higher prices and quantities, and consequently in higher willingness to offer. Finally, we use our experimental data to test existing theories on mood effect. We find that negative affect is related to momentum trading, while positive affect is associated with information processing.

To our knowledge, this is the first work that studies the effect of mood on traders' behavior, rather than market outcomes.

The purpose of this study is to analyze whether differences in market-wide levels of investor personality influence experimental asset market outcomes in terms of limit orders, price levels and price bubbles.

Investor personality is determined by a questionnaire. These data are combined with data from 17 experimental asset markets. Two approaches are used to estimate market-wide levels of investor personality. First, the market-wide average of each personality trait is determined; second, the percentage of individuals with comparable personality in a market is computed. Overall, 364 undergraduate business students participated in the questionnaire and the experimental asset markets.

Limits and transaction prices are higher in markets with higher mean values in participants’ extraversion and openness to experience and lower mean values in participants’ agreeableness and neuroticism. In markets with lower mean values of subjects’ openness to experiences more overpriced transactions are observed. In markets with a higher proportion of extraverted subjects and a lower proportion of neurotic subjects higher limits and transaction prices are observed. Bubble phases last longer in markets with a higher proportion of extraverted and a lower proportion of neurotic subjects.

Overall, the findings suggest that market-wide personality levels influence market outcomes. As a consequence, market-wide levels of personality help to explain prices in auctions with limited number of participants. Additionally, studies that analyze the influence of subjects’ characteristics, including risk aversion, emotional states or overconfidence, on market outcomes should also consider personality traits as potential underlying factor.

The security of e‐commerce is a serious concern of all the major players in the digital business arena who rely heavily on distributed processing in their routine daily operational chores. The security breaches and the related frauds have cost billions of dollars to the businesses and the industries as a whole and consumers suffer also. It is possible that business models of transaction processing that are viable in conventional commerce might be wrong abinitio in the context of digital business. There is a growing need for robust tools and equally rigorous auditable methodologies in the design and verification of the correctness of the digital processing systems that operate over the Internet. This paper focuses on the economic reasoning of business process design. The author has decided to develop a secure online auction protocol as an attempt to apply the design of mechanism reasoning framework in the direction of information systems audit and control of e‐commerce.

The purpose of this paper is to present implications of the seller's ability to bid in the four classical auction forms, with independent private values: English, Dutch, first‐ and second‐price auctions.

Under each auction form, the identity of the winning bidder and the expected winning bid are compared between the case when the seller may bid and when he cannot, using equilibrium bidder strategies. The seller's incentive to bid is evaluated.

The strategies and the welfare results differ with auction type and underlying information assumptions – bidders are either aware or unaware of the seller's ability to bid. In the Dutch and the first‐price auctions, seller‐bidding does not affect any classical results. In the English and the second‐price auctions, it leads to no lower expected prices than without it and higher prices with positive probability. In the English and the second‐price auctions, the seller bids above his reservation value and may unintentionally win the auction. These auctions result in inefficiency with positive probability.

The English and the second‐price auctions are the most common real‐world auctions. In these auctions, the seller's ability to bid – secretly or publicly – redistributes welfare among participants and introduces a possibility of inefficiency. Making this ability publicly known does not solve the latter problem. Auctioneers must prevent the seller from bidding, or must select a different auction form when seller‐bidding is anticipated.

The paper clarifies to regulators, auction designers, bidders, and other readers which auction forms are susceptible to subversion by seller‐bidding and what the potential damages are.

Provides a review of basic appraisal and statistical principles combined with a simple numerical simulation. Suggests two main directions in which the appraisal profession should move. First, because public stock exchanges tend to be more informationally efficient than private property markets, appraisal accuracy can be improved by making increased quantitative use of the information in the share prices of publicly‐traded property companies. Second, it must be recognized that the valuation techniques which are optimal for individual property appraisal are suboptimal for the mass appraisal of aggregate portfolios or indexes of property market values. Suggests that regression‐based techniques based purely on transaction prices may be superior for this latter function.