An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Multi-Objective Calibration For Agent-Based Models

Agent-based modelling is already proving to be an immensely useful tool for scientific and industrial modelling applications. Whilst the building of such models will always be something between an art and a science, once a detailed model has been built, the process of parameter calibration should be performed as precisely as possible. This task is often made difficult by the proliferation of model parameters with non-linear interactions. In addition to this, these models generate a large number of outputs, and their ‘accuracy’ can be measured by many different, often conflicting, criteria. In this paper we demonstrate the use of multi-objective optimisation tools to calibrate just such an agent-based model. We use an agent-based model of a financial market as an exemplar and calibrate the model using a multi-objective genetic algorithm. The technique is automated and requires no explicit weighting of criteria prior to calibration. The final choice of parameter set can be made after calibration with the additional input of the domain expert

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Electricity Network Scenarios for Great Britain in 2050

The next fifty years are likely to see great developments in the technologies deployed in electricity systems, with consequent changes in the structure and operation of power networks. This paper, which forms a chapter in the forthcoming book Future Electricity Technologies and Systems, develops and presents six possible future electricity industry scenarios for Great Britain, focussed on the year 2050. The paper draws upon discussions of important technologies presented by expert authors in other chapters of the book to consider the impact of different combinations of key influences on the nature of the power system in 2050. For each scenario there is a discussion of the effects of the key parameters, with a description and pictorial illustration. Summary tables identify the role of the technologies presented in other chapters of the book, and list important figures of interest, such as the capacity and energy production of renewable generation technologies

Born to trade: a genetically evolved keyword bidder for sponsored search

In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click. In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009

Optimal algorithmic trading and market microstructure

The efficient frontier is a core concept in Modern Portfolio Theory. Based on this idea, we will construct optimal trading curves for different types of portfolios. These curves correspond to the algorithmic trading strategies that minimize the expected transaction costs, i.e. the joint effect of market impact and market risk. We will study five portfolio trading strategies. For the first three (single-asset, general multi-asseet and balanced portfolios) we will assume that the underlyings follow a Gaussian diffusion, whereas for the last two portfolios we will suppose that there exists a combination of assets such that the corresponding portfolio follows a mean-reverting dynamics. The optimal trading curves can be computed by solving an N-dimensional optimization problem, where N is the (pre-determined) number of trading times. We will solve the recursive algorithm using the "shooting method", a numerical technique for differential equations. This method has the advantage that its corresponding equation is always one-dimensional regardless of the number of trading times N. This novel approach could be appealing for high-frequency traders and electronic brokers.quantitative finance; optimal trading; algorithmic trading; systematic trading; market microstructure

Menjana pemodulatan lebar denyut (PWM) penyongsang tiga fasa menggunakan pemproses isyarat digital (DSP)

Baru-baru ini, penyongsang digunakan secara meluas dalam aplikasi industri. Walaubagaimanapun, teknik Pemodulatan Lebar Denyut (PWM) diperlukan untuk mengawal voltan keluaran dan frekuensi penyongsang. Dalam tesis ini, untuk Pemodulatan Lebar Denyut Sinus Unipolar (SPWM) penyongsang tiga fasa adalah dicadang menggunakan Pemproses Isyarat Digital (DSP). Satu model simulasi menggunakan MATLAB Simulink dibangunkan untuk menentukan program Pemodulatan Lebar Denyut Sinus Unipolar (SPWM) Program ini kemudian dibangunkan dalam Pemproses Isyarat Digital (DSP) TMS320f28335. Hasilnya menunjukkan bahawa voltan keluaran penyongsang tiga fasa boleh dikendalikan

Large-scale Complex IT Systems

This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that identifies the major challenges and issues in the development of large-scale complex, software-intensive systems. Central to this is the notion that we cannot separate software from the socio-technical environment in which it is used.Comment: 12 pages, 2 figure