Agent-orientated auction mechanism and strategy design

Agent-based technology is playing an increasingly important role in today’s economy. Usually a multi-agent system is needed to model an economic system such as a market system, in which heterogeneous trading agents interact with each other autonomously. Two questions often need to be answered regarding such systems: 1) How to design an interacting mechanism that facilitates efficient resource allocation among usually self-interested trading agents? 2) How to design an effective strategy in some specific market mechanisms for an agent to maximise its economic returns? For automated market systems, auction is the most popular mechanism to solve resource allocation problems among their participants. However, auction comes in hundreds of different formats, in which some are better than others in terms of not only the allocative efficiency but also other properties e.g., whether it generates high revenue for the auctioneer, whether it induces stable behaviour of the bidders. In addition, different strategies result in very different performance under the same auction rules. With this background, we are inevitably intrigued to investigate auction mechanism and strategy designs for agent-based economics. The international Trading Agent Competition (TAC) Ad Auction (AA) competition provides a very useful platform to develop and test agent strategies in Generalised Second Price auction (GSP). AstonTAC, the runner-up of TAC AA 2009, is a successful advertiser agent designed for GSP-based keyword auction. In particular, AstonTAC generates adaptive bid prices according to the Market-based Value Per Click and selects a set of keyword queries with highest expected profit to bid on to maximise its expected profit under the limit of conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments. The TAC CAT tournament provides an environment for investigating the optimal design of mechanisms for double auction markets. AstonCAT-Plus is the post-tournament version of the specialist developed for CAT 2010. In our experiments, AstonCAT-Plus not only outperforms most specialist agents designed by other institutions but also achieves high allocative efficiencies, transaction success rates and average trader profits. Moreover, we reveal some insights of the CAT: 1) successful markets should maintain a stable and high market share of intra-marginal traders; 2) a specialist’s performance is dependent on the distribution of trading strategies. However, typical double auction models assume trading agents have a fixed trading direction of either buy or sell. With this limitation they cannot directly reflect the fact that traders in financial markets (the most popular application of double auction) decide their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Experiments are conducted under both dynamic and static settings of the continuous BDA market. We find that the allocative efficiency of a continuous BDA market mainly comes from rational selection of trading directions. Furthermore, we introduce a high-performance Kernel trading strategy in the BDA market which uses kernel probability density estimator built on historical transaction data to decide optimal order prices. Kernel trading strategy outperforms some popular intelligent double auction trading strategies including ZIP, GD and RE in the continuous BDA market by making the highest profit in static games and obtaining the best wealth in dynamic games.

Momentum profits and macroeconomic factors

This article tests whether macroeconomic variables and market sentiment influence the size of momentum profits. It finds that although returns to the winner and loser portfolios are influenced by a range of macroeconomic and market wide variables; momentum profits are influenced only by the scale of portfolio outflows. Thus, when investors are sending their capital elsewhere, reduced funds at home, dampen the profitability of the momentum trading strategy. It also finds that when the market closes, below its opening level in the previous six months, momentum profits are higher, which might be a reflection of mean reversion in the market. © 2004 Taylor and Francis Ltd.

An efficient knowledge transfer solution to a novel SMDP formalization of a broker's decision problem

This paper introduces a new technique for optimizing the trading strategy of brokers that autonomously trade in re- tail and wholesale markets. Simultaneous optimization of re- tail and wholesale strategies has been considered by existing studies as intractable. Therefore, each of these strategies is optimized separately and their interdependence is generally ignored, with resulting broker agents not aiming for a glob- ally optimal retail and wholesale strategy. In this paper, we propose a novel formalization, based on a semi-Markov deci- sion process (SMDP), which globally and simultaneously op- timizes retail and wholesale strategies. The SMDP is solved using hierarchical reinforcement learning (HRL) in multi- agent environments. To address the curse of dimensionality, which arises when applying SMDP and HRL to complex de- cision problems, we propose an ecient knowledge transfer approach. This enables the reuse of learned trading skills in order to speed up the learning in new markets, at the same time as making the broker transportable across market envi- ronments. The proposed SMDP-broker has been thoroughly evaluated in two well-established multi-agent simulation en- vironments within the Trading Agent Competition (TAC) community. Analysis of controlled experiments shows that this broker can outperform the top TAC-brokers. More- over, our broker is able to perform well in a wide range of environments by re-using knowledge acquired in previously experienced settings.

Optimising market share and profit margin:SMDP-based tariff pricing under the smart grid paradigm

Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings.

Bi-directional double auction for financial market simulation

Typical Double Auction (DA) models assume that trading agents are one-way traders. With this limitation, they cannot directly reflect the fact individual traders in financial markets (the most popular application of double auction) choose their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Based on experiments under both static and dynamic settings, we find that the allocative efficiency of a static continuous BDA market comes from rational selection of trading directions and is negatively related to the intelligence of trading strategies. Moreover, we introduce Kernel trading strategy designed based on probability density estimation for general DA market. Our experiments show it outperforms some intelligent DA market trading strategies. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Enterprise strategy development:a three-dimensional approach to the role of SCM

The Irish economy has grown strongly in recent years. This, combined with other factors in the international trading environment, has sharpened the focus on the need for innovative strategies at a national level which can contribute positively from an enterprise strategy development perspective. As traditional manufacturing activity has migrated to lower labour cost economies questions have been raised concerning the role of supply chain management (SCM) in the evolving Irish business environment. This article describes some of the main drivers in terms of both SCM and the changing economic and business environment. It goes on to propose a three-dimensional approach to understanding the potential role of SCM in the new scenario. This in turn informs the logistics and SCM research agenda from a national policy point of view.

The role of supply chain management in Irish enterprise strategy development:a three-dimensional approach

The Irish economy has grown strongly in recent years. This, combined with other factors in the international trading environment, has sharpened the focus on the need for innovative strategies at a national level which can contribute positively from an enterprise strategy development perspective. As traditional manufacturing activity has migrated to lower labour cost economies questions have been raised concerning the role of supply chain management (SCM) in the evolving Irish business environment. This paper describes some of the main drivers in terms of both SCM and the changing economic and business environment. It goes on to propose a three-dimensional approach to understanding the potential role of SCM in the new scenario. This in turn informs the logistics and SCM research agenda from a national policy point of view.

An intelligent broker agent for energy trading:an MDP approach

This paper details the development and evaluation of AstonTAC, an energy broker that successfully participated in the 2012 Power Trading Agent Competition (Power TAC). AstonTAC buys electrical energy from the wholesale market and sells it in the retail market. The main focus of the paper is on the broker’s bidding strategy in the wholesale market. In particular, it employs Markov Decision Processes (MDP) to purchase energy at low prices in a day-ahead power wholesale market, and keeps energy supply and demand balanced. Moreover, we explain how the agent uses Non-Homogeneous Hidden Markov Model (NHHMM) to forecast energy demand and price. An evaluation and analysis of the 2012 Power TAC finals show that AstonTAC is the only agent that can buy energy at low price in the wholesale market and keep energy imbalance low.

Trading-up on unmet expectations? Evaluating consumers’ expectations in online premium grocery shopping logistics

Geography and retail store locations are inherently bound together; this study links food retail changes to systemic logistics changes in an emerging market. The later include raising income and education, access to a wide range of technologies, traffic and transport difficulties, lagging retail provision, changing family structure and roles, as well as changing food culture and taste. The study incorporates demand for premium products defined by Kapferer and Bastien [2009b. The Luxury Strategy. London: Kogan Page] as comprising a broad variety of higher quality and unique or distinctive products and brands including in grocery organic ranges, healthy options, allergy free selections, and international and gourmet/specialty products through an online grocery model (n = 356) that integrates a novel view of home delivery in Istanbul. More importantly from a logistic perspective our model incorporates any products from any online vendors broadening the range beyond listed items found in any traditional online supermarkets. Data collected via phone survey and analysed via structural equation modelling suggest that the offer of online premium products significantly affects consumers’ delivery logistics expectations. We discuss logistics operations and business management implications, identifying the emerging geography of logistic models which respond to consumers’ unmet expectations using multiple sourcing and consolidation points.