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.

The role of pre-existing liquidity in determining pricing efficiency and liquidity gains following the introduction of SETSmm

In this paper we examine the impact that the new trading system SETSmm had on market quality measures such as firm value, liquidity and pricing efficiency. This system was introduced for mid-cap securities on the London Stock Exchange in 2003. We show that there is a small SETSmm return premium associated with the announcement that securities are to migrate to the new trading system. We find that migration to SETSmm also improves liquidity and pricing efficiency and these changes are related to the return premium. We also find that these gains are stronger for firms with high pre SETSmm liquidity and weaker for firms with low SETSmm liquidity.

Intraday trading patterns in London listed exchange traded funds

In this paper we examine the intraday trading patterns of Exchange Traded Funds (ETFs) listed on the London Stock Exchange. ETFs have been shown to be characterised by much lower bid–ask spread costs and by lower levels of information asymmetry than individual securities. One possible explanation for intraday trading patterns is that concentration of trading arises at the start of the trading day because informed traders have private information that quickly diminishes in value as trading progresses. Since ETFs have lower trading costs and lower levels of information asymmetry we would expect these securities to display less pronounced intraday patterns than individual securities. We fail to find that ETFs are characterised by concentrated trading bouts during the day and therefore find support for the argument that information asymmetry is the cause of intraday volume patterns in stock markets. We find that ETF bid–ask spreads and volatility are elevated at the open but not at the close. This lends support to the “accumulation of information” explanation that sees high spreads and volatility at the open as a consequence of information accumulating during a market closure and impacting on the market when it next opens.

Advanced education and training in bioenergy in Europe

The paper explains how bioenergy education and training is growing in Europe. Employment estimates are included for renewable energy in general, and bioenergy in particular, to highlight the need for a broadly based education and training programme that is essential to build a knowledgeable workforce that can drive Europe's growing bioenergy sector. The paper reviews current provisions in bioenergy at Masters and PhD levels across the 27 members of the EU (EU27) plus Norway and Switzerland. This identifies a very active and expanding bioenergy education provision. 65 English-language Masters Courses in bioenergy (either focussing completely on bioenergy or with significant bioenergy content or specialisation) were identified. 231 providers of PhD studies in bioenergy were found.Masters Course offerings have grown rapidly across Europe during the last five years, but where data is available, enrolment has been quite low suggesting that there is an oversupply of courses and that course organisers are being optimistic in their projections. Existing provisions in Europe at Masters and PhD levels are clearly more than sufficient for short term needs, but further work is needed to evaluate the take-up rate and the content and focus of the provisions. To ensure talented graduates are attracted to these programmes, better promotion, stronger links with the research community and industry, and increased collaboration among course providers are needed. Short Courses of two to five days are an excellent way of meeting post-experience training needs but require further growth and development to serve the needs of the bioenergy community. © 2011 Elsevier Ltd.

A review of research and practice for the industrial networks of the future

Academic researchers have followed closely the interest of companies in establishing industrial networks by studying aspects such as social interaction and contractual relationships. But what patterns underlie the emergence of industrial networks and what support should research provide for practitioners? First, it appears that manufacturing is becoming a commodity rather than a unique capability, which accounts especially for low-technology approaches in downstream parts of the network, for example, in assembly operations. Second, the increased tendency towards specialisation has forced other, upstream, parts of industrial networks to introduce advanced manufacturing technologies for niche markets. Third, the capital market for investments in capacity, and the trade in manufacturing as a commodity, dominates resource allocation to a larger extent than was previously the case. Fourth, there is becoming a continuous move towards more loosely connected entities that comprise manufacturing networks. Finally, in these networks, concepts for supply chain management should address collaboration and information technology that supports decentralised decision-making, in particular to address sustainable and green supply chains. More traditional concepts, such as the keiretsu and chaibol networks of some Asian economies, do not sufficiently support the demands now being placed on networks. Research should address these five fundamental challenges to prepare for the industrial networks of 2020 and beyond. © 2010 Springer-Verlag London.

Stock repurchases and treasury share sales:do they stabilize price and enhance liquidity?

Can companies reduce the volatility and increase the liquidity of their stocks by trading them? In the context of the Italian stock market, where companies have far more leeway to sell as well as buy their own stocks than in the U.S., the answer is yes. We examine the effects of trading (open-market share repurchases and treasury shares sales) on liquidity (bid–ask spread) and volatility (return variance). Further, we examine the impact of shareholder approvals of repurchase programs on liquidity and volatility. We find clear evidence that trading increases liquidity and reduces volatility. These results are consistent with our analysis of the motives Italian companies give for making share repurchases.

The Rover Task Force:a case study in proactive and reactive policy intervention?

The paper examines the policy responses in the UK West Midlands to the successive crises at the car maker MG-Rover. Whilst the firm’s eventual collapse in 2005 was a substantial shock to the West Midlands economy, the impact was much less than was anticipated when the firm was first threatened with closure in 2000 at the time of its break-up and sale by the German car firm BMW. Although the firm struggled as an independent producer, the five years of continued production until 2005 and the work of the initial Rover Task Force (RTF1), enabled many suppliers to adjust and diversify away from their hitherto dependence on MG-Rover resulting in as many as 10,000–12,000 jobs being ‘saved’. This first intervention was later followed by a programme to help ex-workers to find new jobs or re-train and assist supply firms to continue trading in the short term. Examination of the effectiveness of these emergency initiatives enables a wider discussion about the nature of industrial policy in the region and the work of the local regional development agency’s cluster-based approach to economic development and business support. Whilst the actions taken were successful in a number of aspects, there were a number of significant ‘failures’ at both national and local level. The MG-Rover case also illustrates a number of critical issues pertaining to regionally based cluster policies and the organization of cluster management groups where the ‘cluster’ in question not only crosses both administrative and ‘sector’ boundaries but is also subject to the imperatives of the global market car market.

Modelling intraday trading volume

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Reinsurance trading in Lloyd's of London:balancing conflicting-yet-complementary logics in practice

Drawing on a year-long ethnographic study of reinsurance trading in Lloyd’s of London, this paper makes three contributions to current discussions of institutional complexity. First, we shift focus away from structural and relatively static organizational responses to institutional complexity and identify three balancing mechanisms - segmenting, bridging, and demarcating - which allow individuals to manage competing logics and their shifting salience within their everyday work. Second, we integrate these mechanisms in a theoretical model that explains how individuals can continually keep coexisting logics, and their tendencies to either blend or disconnect, in a state of dynamic tension which makes them conflicting-yet-complementary logics. Our model shows how actors are able to dynamically balance coexisting logics, maintaining the distinction between them, whilst also exploiting the benefits of their interdependence. Third, in contrast to most studies of newly formed hybrids and/or novel complexity our focus on a long-standing context of institutional complexity shows how institutional complexity can itself become institutionalized and routinely enacted within everyday practice.

The role of pre-existing liquidity in determining price efficiency and liquidity gains following the introduction of SETSmm

In this paper we examine the impact that the new trading system SETSmm had on market quality measures such as firm value, liquidity and pricing efficiency. This system was introduced for mid-cap securities on the London Stock Exchange in 2003. We show that there is a small SETSmm return premium associated with the announcement that securities are to migrate to the new trading system. We find that migration to SETSmm also improves liquidity and pricing efficiency and these changes are related to the return premium. We also find that these gains are stronger for firms with high pre SETSmm liquidity and weaker for firms with low SETSmm liquidity. © 2013 John Wiley & Sons Ltd.

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.

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.

The effects of non-trading on the illiquidity ratio

Using a simulation analysis we show that non-trading can cause an overstatement of the observed illiquidity ratio. Our paper shows how this overstatement can be eliminated with a very simple adjustment to the Amihud illiquidity ratio. We find that the adjustment improves the relationship between the illiquidity ratio and measures of illiquidity calculated from transaction data. Asset pricing tests show that without the adjustment, illiquidity premia estimates can be understated by more than 17% for NYSE securities and by more than 24% for NASDAQ securities.