主讲人：Edward Tsang教授 

    讲座主题：The Repository Method: finding scarce investment opportunities（智能方                法：寻找奇缺投资的机会） 

    时间：9月20号（本周四）晚19：30～21：30 

    地点：一号楼学术报告厅

    主讲人介绍： 
    Edward Tsang holds a first degree in Business Administration (major in Finance) and a PhD in Computer Science. He is currently a Professor in Computer Science at University of Essex. He is also the Deputy Director of Centre for Computational Finance and Economic Agents (CCFEA, http://www.cfea-labs.net), an interdisciplinary research centre that applies artificial intelligence methods finance and economics. CCFEA is supported by City Associates, members of which include Barclays, HSBC, Bank of England, and others City firms.  

    Edward Tsang has broad interest in artificial intelligence, which includes heuristic search, computational finance, economic agents, constraint satisfaction, combinatorial optimisation,scheduling, evolutionary computation and automated bargaining. He established and leads the Constraint Satisfaction and Optimisation Research Group and the Computational Finance Research Group at University of Essex. 
 
    Edward Tsang chaired the Technical Committee in Computation Finance and Economics in IEEE’s Computational Intelligence Society in 2004 and 2005. He is an editor of IEEE Transactions in Evolutionary Computation, the Constraints journal, the Journal of Scheduling and The Journal of Management and Economics. He has served committees and panels to many major international conferences and workshops.  

    Edward Tsang works closely with industry. He had over five years experience in the commercial sector in Hong Kong. Furthermore, he has given consultation to GEC Marconi, British Telecom, The Commonwealth Secretariat, WestcomZivo and other organizations. Guided Local Search, developed in his laboratory, has been embedded in ILOG DISPATCHER, a commercial product for vehicle routing. A patent on multi-objective opitmization has been obtained with Honda Research Europe.

翻译为：
    Dr. Edward Tsang任教于英国埃塞克斯郡大学，计算机科学系 (Department of Computer Science, University of Essex) ，是目前将遗传规划 (GP, genetic programming) 应用在财务市场有卓越成就的专家之一。Dr. Tsang是计算财务及经济代理人中心(CCFEA，http://www.cfea-labs.net)副主任。 CCFEA是跨学科的研究中心，把人工智能方法用在财务及经济学方面问题。已经取得许多单位的合作。教授在埃塞克斯郡大学建立并领导限制条件满意和最优化研究群和计算财务研究群(Constraint Satisfaction and Optimisation Research Group and the Computational Finance Research Group)。他对人工智能方法的研究兴趣十分广泛，包括启发式的搜寻、计算财务、经济代理人、限制条件最适化、组合最优化、排程、演化的计算以及自动化竞价等，都是目前热门的研究课题。

    他目前担任CONSTRAINTS(Kluwer)，Journal of Scheduling（John Wiley & Sons, Ltd.），IEEE Transactions on Evolutionary Computation ，International Journal of Computational Intelligence， Journal of Management and Economics（electronic journal）等杂志的编委，IEEE NNS Task Force on Evolutionary Computation in Finance and Economics主席。

    Edward　Tsang 与工业界联系紧密。他在香港商界有5年的经历，并且他给GEC Marconi, British Telecom, The Commonwealth Secretariat, WestcomZivo和其他组织做过顾问。在他实验室开发的Guided Local Search已经嵌入到ILOG DISPATCHER（交通领域多目标优化方面的专利商品），被honda欧洲研发中心得到。

    之前做过的演讲：
             The Repository Method: finding scarce investment opportunities
 
                               Professor Edward Tsang
                                 University of Essex
 
    Suppose I make a prediction that the stock market will not crash tomorrow. The chance of this prediction being correct is rather high. But if I make the same prediction every day, I'll probably be wrong one day -- historically the stock market does crash occasionally. Although my predictions have a high rate of correctness, such predictions are rather useless to anybody. This is because they fail to pick out events that matter -- namely crashes in the market.  

    Picking out scarce events is the subject of study in a new interdisciplinary research called "Chance Discovery. In this talk, I shall introduce the Repository Method, a novel method for chance discovery. This method is based on genetic programming. Genetic programming is a branch of evolutionary computation, which base its idea on Darwin's evolutionary theory. We have 
demonstrated in our previous work, EDDIE, that genetic programming can be an effective tool in machine learning in finance. The Repository Method starts where genetic programming ends: decision trees produced by EDDIE can be further processed to improve their performance. In particularly, they can be enhanced for finding investment opportunities with exceptionally high returns in financial forecasting. It can also be used to generate an ensemble of classification systems to suit different risk preferences in investment.

    我做出这样一个预测：股票市场明天不会瘫痪，那么我这个预测正确的可能性会非常高。但是如果我天天做这样的预测，很有可能，某一天我就会犯错。历史表明，股票市场偶尔会瘫痪。尽管我有一个这样高正确率的预测，但是我这个预测对很多人是没有用的。这是因为他们没有获取到市场瘫痪这一信息。 

    获取小概率事件是一个名为“Chance Discovery”的新兴交叉学科的研究课题。在这个话题讨论中，我将介绍一种智能算法：Chance Discovery的一种新颖算法。这种算法的基础是遗传规则，遗传规则是演化算法的一个分支，而演化算法又是以达尔文进化论为理论基础的。我们以前的研究工作EDDIE表明，遗传规则是计算财务的一个重要工具。遗传规则的结束标志着智能算法的开始：从EDDIE得到的决策图标能进一步证明遗传规则的作用。特别是，在金融预测上，它们能促进寻找投资机会，从而获得超出预测的高的回报。此外，它们还可以形成一个新的分类学科来应对不同的风险投资。
    Profile, Edward Tsang
    URL: http://cswww.essex.ac.uk/CSP/edward

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                                                                        研究生工作部
                                                                        2007.9.17