Trading Agent Research Articles

Those Who Know Most: Insider Trading in Eighteenth-Century Amsterdam

This paper studies how private information is incorporated into prices, using a unique setting from the eighteenth century that is closer to stylized models of price discovery than modern-day markets. Specifically, the paper looks at English securities traded in both London and Amsterdam. Private information reached Amsterdam through sailing boats that sailed only twice a week and in adverse weather could not sail at all. Results are consistent with a Kyle model in which informed agents trade strategically. Most importantly, the speed of information revelation in Amsterdam depended on the expected time until the private signal would become public.

Explaining job satisfaction and intentions to quit from a value-risk perspective

PurposeThe purpose of this paper is to investigate the effect of risk aversion (RA) on expected income and job satisfaction (JS) with pay in the case of sales agents under a compensation system based on pay-for-performance.Design/methodology/approachData were collected from 125 sales agents of an outbound call center via questionnaires and controlled experiments. Seemingly unrelated equations using maximum likelihood estimation was employed to estimate the proposed model and test relationships.FindingsFindings show that income expectations (IE) respond to a model of trade-off between value and risk. The sales agents trade off their expected value of performance (i.e. expected income) with RA. Additionally, IE and actual performance of the salesperson have influence on JS with pay with opposite signs.Research limitations/implicationsThe results of this research may need to be modified to consider jobs with compensation systems with a higher proportion of fixed component of the wage than the variable component. Also, a broader concept of JS and not just related to the pay, should be considered.Practical implicationsGiven the importance of RA in the attitudes of employees in relation to their expectations, the authors believe that it should be necessary and useful to incorporate measures of RA in the process of selection and recruitment for these jobs.Originality/valueThis paper assessed an important element as the RA at the micro level inside of an organization. This element could be very important for job environments with high uncertainty in income that could influence JS via employee expectations.

Tradable planning permits versus auctioned tradable development rights: different trading agents, different policy outcomes

The tradable planning permit (TPP) policy seeks to limit the land take by development. It can be used in countries where development rights are vested by land-use planning to certain landowners only. TPPs are traded among public authorities. We introduce here a new policy, called the auctioned tradable development rights (ATDR) policy, which takes advantage of unvested rights of development and of trading among private actors. We compare the TPP, the ATDR policy and our baseline land-use planning model using a set of criteria. There are trade-offs within compared models. Both trading policies extend opportunities for land protection from development in exchange for higher transaction costs (highest in the ATDR policy). Trading policies seek also to recapture a part of development rent, decrease rent-seeking, and locate new development more effectively from the investors' perspective. However, trading among public authorities in the TPP model may hinder attaining these effects.

MONEY ILLUSION AND MARKET SURVIVAL

By studying complete and incomplete dynamic financial markets, I show that if some agents are money-illusioned and neglect inflation, the rational agents who are aware of inflation are driven out of the market in the long run, in the sense that the money-illusioned agents consume the economy's entire endowment. The reason for this finding is that with inflation, the money-illusioned agents always believe that the return on their savings is higher than it actually is. Because these agents trade financial assets in markets with the rational agents, the rational agents end up being borrowers and the money-illusioned agents lenders. Because the rational agents' debt accumulates over time, they become so indebted that the money-illusioned agents eventually consume the economy's entire endowment. If there is deflation, the opposite is true, and the rational agents evolutionarily dominate.

Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game

Prediction markets offer an efficient market-based mechanism to aggregate large amounts of dispersed or distributed information from different people to predict the possible outcome of future events. Recently, automated prediction markets where software trading agents perform market operations such as trading and updating beliefs on behalf of humans have been proposed. A challenging aspect in automated prediction markets is to develop suitable techniques that can be used by automated trading agents to update the price at which they should trade securities related to an event so that they can increase their profit. This problem is nontrivial, as the decision to trade and the price at which trading should occur depends on several dynamic factors, such as incoming information related to the event for which the security is being traded, the belief-update mechanism and risk attitude of the trading agent, and the trading decision and trading prices of other agents. To address this problem, we have proposed a new behavior model for trading agents based on a game-theoretic framework called partially observable stochastic game with information (POSGI). We propose a correlated equilibrium (CE)-based solution strategy for this game that allows each agent to dynamically choose an action (to buy or sell or hold) in the prediction market. We have also performed extensive simulation experiments using the data obtained from the Intrade prediction market for four different prediction markets. Our results show that our POSGI model and CE strategy produces prices that are strongly correlated with the prices of the real prediction markets. Results comparing our CE strategy with five other strategies commonly used in similar market show that our CE strategy improves price predictions and provides higher utilities to the agents compared to other existing strategies.

Buying time: futures trading and telegraphy in nineteenth-century global commodity markets

AbstractAdapting the dictum that ‘time is money’, Western merchants have long promoted and welcomed technologies to accelerate commerce. Thanks to revolutionary changes in communication in the nineteenth century, including the telegraph, information could for the first time travel much faster around the globe than goods. This asymmetric time–space compression created new problems for agents of global trade, as transactions could occur faster than the handling of the goods. Yet this problem also presented an opportunity to establish new forms of trading and ways of thinking about time. In the case of world markets for primary products such as grain or cotton, the new technique of futures trading at commodity exchanges became a tool to redress the time differential between the movement of goods and information. It pushed the commercial community to think in a ‘double time’ of a dematerialized present and a material future with physical goods, and centred global commodity markets on a few marketplaces. The article thus argues that historians need to examine economic understandings of the future alongside political, cultural, and social understandings.

Stochastic simulation framework for the limit order book using liquidity-motivated agents

In this paper, we develop a new form of simulation model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimization. We then demonstrate how such a modeling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios.

Agent-based model with multi-level herding for complex financial systems.

In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

The 2015 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2015 (Power TAC 2015). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot.The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins.

Asset Pricing and Asymmetric Reasoning

We present a theory and experimental evidence on pricing and portfolio choices under asymmetric reasoning. We show that under asymmetric reasoning, prices do not reflect all (types of) reasoning. Some agents who observe prices that cannot be reconciled with their reasoning switch from perceiving the environment as risky to perceiving it as ambiguous. If they are ambiguity-averse, these agents become price-insensitive. Results from an experiment show that, consistent with the theory, (i) without aggregate risk, mispricing decreases as the fraction of price-sensitive agents increases; and (ii) with aggregate risk, price-insensitive agents trade to more balanced portfolios.

Stochastic Simulation Framework for the Limit Order Book Using Liquidity Motivated Agents

In this paper we develop a new form of agent-based model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimisation. We then demonstrate how such an agent-based modelling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios.

Katalog der Textilreste

In this contribution Mycenaean clay figurines are examined in the light of their Mediterranean, and especially their central Mediterranean distributions. In Italy a recent find from Fondo Paviani (Verona) adds to the small group of figurines already known from Lipari, Scoglio del Tonno and possibly Broglio di Trebisacce. Following the prevailing view which associates figurines with the palaces of the Argolis and their ideological discourse, the few examples from Italy may be used to confirm that exchange relationships between the Mycenaeans and the Italian communities were unofficial and unsystematic. The contexts of the figurines – mostly from sites to which Mycenaean pottery was exclusively or mainly imported - may however indirectly testify to a concrete function in trade activities. More generally, Mycenaean figurines may have conveyed an important meaning in sanctioning transactions at various levels – from the personal and private to the official and public – and in various activities, religious as well as purely mundane. As for long-distance exchange, an important link between figurines and trade agents is also indicated. Wherever Mycenaean pottery is mainly attested by local production, implying the transfer of people or technologies – as in Italy or Anatolia - figurines seem to be under-represented, whereas they are more numerous where pottery is clearly an outcome of trade, as in the Levant. Finally, a role in exchange activities is also suggested for the rough locallymade Italian figurines, which are mainly distributed along the Adriatic coast, at several sites engaged in industrial production and trade during the Italian Final Bronze Age, when Italian style seems to gain importance in the Aegean.

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions.

Bitcoin‐Based Decentralized Carbon Emissions Trading Infrastructure Model

ABSTRACTThis paper presents a system‐of‐systems architecture model for a Decentralized Carbon Emissions Trading Infrastructure (D‐CETI) with focus on privacy and system security goals. The structure and behavior are implemented as a solution to the problem of trading carbon emissions anonymously among the trading agents. Privacy and security of the trading agents and their carbon credits are the main requirements behind the architecture of D‐CETI. The decentralized structure of multiple systems and distributed behavior are the two main features of D‐CETI that distinguish it from the traditional carbon trading schemes and protocols. D‐CETI is based on Bitcoin, a peer‐to‐peer digital currency with no central authority, and Open Transactions, a system that simplifies the use of cryptography in financial transactions. The architecture of D‐CETI is evaluated and compared with the architecture of five other carbon emissions trading platforms.

El efecto del anuncio de beneficios cuando cotizan opciones: un estudio conjunto del mercado de contado y el mercado de opciones

This study investigates the informational role of option trading in price discovery process around the dissemination of earnings news. The effect of options markets are examined first by analysing stock market reaction to earnings news conditional on the availability of option markets. Study methodology has been used and distribution functions compared. Option trading activity around the release of earnings news was then examined. Results show that, when option trading is available the option market enhances the price efficiency of equity markets. Moreover, the dissemination of earnings news is associated with a significant unusual activity in market option. The results support the improvement of the efficiency of equity markets after options listing, and that informed agents trade in options markets in order to take advantage of relevant company information.

Smart Grid Challenges for Electricity Retailers

The need to achieve sustainability is driving a major transformation of the energy sector. The traditional top-down approach to electricity supply and grid management is being strongly disrupted by a range of forces including distributed renewables, retail market liberalization, and the need for energy consumers to adapt their behavior to the availability of renewable energy sources. We introduce Power TAC, a competitive simulation that challenges researchers to build autonomous trading agents that tackle the complex decision processes a retailer will need to face in future competitive retail electricity markets.

TacTex'13: A Champion Adaptive Power Trading Agent

Sustainable energy systems of the future will no longer be able to rely on the current paradigm that energy supply follows demand. Many of the renewable energy resources do not produce power on demand, and therefore there is a need for new market structures that motivate sustainable behaviors by participants. The Power Trading Agent Competition (Power TAC) is a new annual competition that focuses on the design and operation of future retail power markets, specifically in smart grid environments with renewable energy production, smart metering, and autonomous agents acting on behalf of customers and retailers. It uses a rich, open-source simulation platform that is based on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making, as well as the robustness of proposed market designs. This paper introduces TacTex'13, the champion agent from the inaugural competition in 2013. TacTex'13 learns and adapts to the environment in which it operates, by heavily relying on reinforcement learning and prediction methods. This paper describes the constituent components of TacTex'13 and examines its success through analysis of competition results and subsequent controlled experiments.

A study on the dispute cases of Foreign Trade Agent System in China and implications for Korean exporters

A study on the dispute cases of Foreign Trade Agent System in China and implications for Korean exporters

The 2014 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2014 (Power TAC 2014). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins.

Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents

The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.