大卫·莱因韦伯一分钟访谈

IF 0.3 Q4 BUSINESS, FINANCE
D. Leinweber
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He is now principal of Leinweber & Co., and in a public service role, co-founder of the Center for Innovative Financial Technology at Lawrence Berkeley Lab. http://www.lbl.gov/CS/CIFT .html LBL is one of the premier data intensive science research facilities in the world. Two of LBL’s most recent Nobel laureates used methods of “data intensive science” for physical problems on some of the largest computers in the world. CIFT is supported by financial firms and foundations. CIFT’s key idea is that systemic risk includes the risk of systems. Jason Zweig of the WSJ put it this way: “Could Computers Protect the Market from Computers?” Leinweber combines the expertise of a computer scientist with the financial experience of a pioneering practitioner in electronic markets and computer driven investing. He has undergraduate degrees in Physics and Computer Science from MIT and a PhD in applied mathematics from Harvard University and is a frequent keynote speaker and writer. He blogs at http://blogs.forbes.com/people/David%20Leinweber/ and http://nerdsonwallstreet.typepad.com. What are your research interests right now? At LBL, we focus on how modern information technologies can make markets more stabile, safe and secure. A collection of well-tested stable systems, in aggregate, and operating on time scales so much faster than their users, can be unstable and unpredictable. Cyber security of markets in aggregate is so complex that it has taken its place at the back of the queue. The stock market is only the most visible because of its high transparency. Resolution of issues in much larger markets, such as swaps (including what were once known as “toxic assets”) in the highly politicized SEF and FPML efforts, as we saw in 2008, can be more damaging than events in the stock market. An amazingly well written and detailed account of these issues in modern markets is found in Scott Paterson’s book “Dark Pools” which is about much more than dark pools in the narrow industry sense. At Leinweber & Co, our commercial work has been looking to extend quant methods into the “quanttextual” world, where information comes as “big data” in the form of words as well as numbers. Understanding complex evolving events are an area where humans still have game against computers. What do you see as academically exciting? I have a Higgs Boson in my sock drawer, but don’t tell. I also have a copy of Emanuel Derman’s rules for financial engineers to avoid bad or catastrophic model behavior. I think they should be tattooed on the inside of the eyelids of all financial engineers. What would you work on if you had lots of time? That is truly a tough question. In the book “How I Became a Quant” http://www.amazon.com/HowBecame-Quant-Insights-Streets/dp/0470050624 I ‘fessed up to being an “accidental quant”, where early work on real-time defense systems at RAND led to early “electronic order working”, which accelerated into algorithmic trading, and HFT (which has both white-hat and black-hat practitioners). In the 90s and early 2000s, I was pleased to tell my kid’s friends what I did for a living in electronic markets, and see from their reactions that I was doing something good. Since 2008, the sign on those reactions has flipped. At the end of the Manhattan Project, just before the test of the first atomic bomb, some physicists were worried that it might set the sky on fire. The non Wall Street twenty-somethings of today seem to feel that the misapplication of financial technology has set their future on fire. It doesn’t have to be this way. 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Jason Zweig of the WSJ put it this way: “Could Computers Protect the Market from Computers?” Leinweber combines the expertise of a computer scientist with the financial experience of a pioneering practitioner in electronic markets and computer driven investing. He has undergraduate degrees in Physics and Computer Science from MIT and a PhD in applied mathematics from Harvard University and is a frequent keynote speaker and writer. He blogs at http://blogs.forbes.com/people/David%20Leinweber/ and http://nerdsonwallstreet.typepad.com. What are your research interests right now? At LBL, we focus on how modern information technologies can make markets more stabile, safe and secure. A collection of well-tested stable systems, in aggregate, and operating on time scales so much faster than their users, can be unstable and unpredictable. Cyber security of markets in aggregate is so complex that it has taken its place at the back of the queue. The stock market is only the most visible because of its high transparency. Resolution of issues in much larger markets, such as swaps (including what were once known as “toxic assets”) in the highly politicized SEF and FPML efforts, as we saw in 2008, can be more damaging than events in the stock market. An amazingly well written and detailed account of these issues in modern markets is found in Scott Paterson’s book “Dark Pools” which is about much more than dark pools in the narrow industry sense. At Leinweber & Co, our commercial work has been looking to extend quant methods into the “quanttextual” world, where information comes as “big data” in the form of words as well as numbers. Understanding complex evolving events are an area where humans still have game against computers. What do you see as academically exciting? I have a Higgs Boson in my sock drawer, but don’t tell. 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At the end of the Manhattan Project, just before the test of the first atomic bomb, some physicists were worried that it might set the sky on fire. The non Wall Street twenty-somethings of today seem to feel that the misapplication of financial technology has set their future on fire. It doesn’t have to be this way. 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引用次数: 0

摘要

在每一期中,Algorithmic Finance都会对我们的顾问或编辑委员会的一位成员或另一位领先的学者或从业者进行简短的采访。这些简短的谈话旨在提供他们当前想法的一瞥。本期,我们将与大卫·莱因韦伯进行对话。——DAVID LEINWEBER,《华尔街的书呆子:数学、机器和有线市场》的作者,http:// tinyurl.com/nerdsonwallst最近被Advanced Trading杂志评为十年十大创新者之一。作为两家金融科技公司的创始人,以及数十亿美元定量股票投资组合的经理,他为创新带来了实用的方法。他现在是Leinweber & Co.的负责人,并担任公共服务职位,是劳伦斯伯克利实验室创新金融技术中心的联合创始人。http://www.lbl.gov/CS/CIFT .html LBL是世界上首屈一指的数据密集型科学研究机构之一。LBL最近的两位诺贝尔奖获得者在世界上一些最大的计算机上使用了“数据密集型科学”的方法来解决物理问题。CIFT是由金融公司和基金会支持的。CIFT的核心理念是,系统性风险包括系统风险。《华尔街日报》的杰森·茨威格(Jason Zweig)这样说:“电脑能保护市场免受电脑的侵害吗?”Leinweber将计算机科学家的专业知识与电子市场和计算机驱动投资领域的先驱从业者的金融经验相结合。他拥有麻省理工学院物理学和计算机科学学士学位,哈佛大学应用数学博士学位,并经常发表主题演讲和写作。他的博客地址是http://blogs.forbes.com/people/David%20Leinweber/和http://nerdsonwallstreet.typepad.com。你现在的研究兴趣是什么?在LBL,我们专注于现代信息技术如何使市场更加稳定、安全。一组经过良好测试的稳定系统,总的来说,在比用户更快的时间尺度上运行,可能是不稳定和不可预测的。总体而言,市场的网络安全是如此复杂,以至于它被排在了队伍的最后。由于其高度的透明度,股票市场是最明显的。在更大的市场中解决问题,比如在高度政治化的SEF和FPML中进行的掉期(包括曾经被称为“有毒资产”的交易),正如我们在2008年看到的那样,可能比股市中的事件更具破坏性。斯科特·帕特森(Scott Paterson)在《暗池》(Dark Pools)一书中对现代市场中的这些问题进行了精彩而详尽的描述,这本书远远超出了狭义的行业意义上的暗池。在Leinweber & Co,我们的商业工作一直在寻求将量化方法扩展到“量子文本”世界,在这个世界中,信息以文字和数字的形式作为“大数据”出现。理解复杂的进化事件仍是人类与计算机较量的领域。你认为什么是学术上令人兴奋的?我放袜子的抽屉里有个希格斯玻色子,但别告诉别人。我还有一份伊曼纽尔·德曼(Emanuel Derman)为金融工程师制定的避免不良或灾难性模型行为的规则。我认为应该把它们纹在所有金融工程师的眼皮内侧。如果你有很多时间,你会做什么?这确实是一个很难回答的问题。在《我如何成为一名量化分析师》(http://www.amazon.com/HowBecame-Quant-Insights-Streets/dp/0470050624)一书中,我承认自己是一名“偶然的量化分析师”,在那里,兰德公司早期在实时防御系统上的工作导致了早期的“电子订单工作”,这加速了算法交易和高频交易(既有白帽交易员也有黑帽交易员)。在90年代和21世纪初,我很高兴地告诉我孩子的朋友们我在电子市场是做什么的,并从他们的反应中看出我在做一件好事。自2008年以来,这些反应的迹象发生了逆转。在曼哈顿计划结束时,就在第一颗原子弹试验之前,一些物理学家担心它可能会点燃天空。如今,20多岁的非华尔街年轻人似乎觉得,金融科技的误用点燃了他们的未来。事情本不必这样的。德曼负责任的信条
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Minute with David Leinweber
In each issue, Algorithmic Finance features a brief interview with one member of our advisory or editorial boards or another leading academic or practitioner. These brief conversations are intended to provide a glimpse of their current thinking. In this issue, we talk with David Leinweber. – DAVID LEINWEBER, author of “Nerds on Wall Street: Math, Machines and Wired Markets”, http:// tinyurl.com/nerdsonwallst was recently named one of the Top Ten Innovators of the Decade by Advanced Trading magazine. As founder of two financial technology firms, and as manager of multi-billon dollar quantitative equity portfolios, he brings a practical approach to innovation. He is now principal of Leinweber & Co., and in a public service role, co-founder of the Center for Innovative Financial Technology at Lawrence Berkeley Lab. http://www.lbl.gov/CS/CIFT .html LBL is one of the premier data intensive science research facilities in the world. Two of LBL’s most recent Nobel laureates used methods of “data intensive science” for physical problems on some of the largest computers in the world. CIFT is supported by financial firms and foundations. CIFT’s key idea is that systemic risk includes the risk of systems. Jason Zweig of the WSJ put it this way: “Could Computers Protect the Market from Computers?” Leinweber combines the expertise of a computer scientist with the financial experience of a pioneering practitioner in electronic markets and computer driven investing. He has undergraduate degrees in Physics and Computer Science from MIT and a PhD in applied mathematics from Harvard University and is a frequent keynote speaker and writer. He blogs at http://blogs.forbes.com/people/David%20Leinweber/ and http://nerdsonwallstreet.typepad.com. What are your research interests right now? At LBL, we focus on how modern information technologies can make markets more stabile, safe and secure. A collection of well-tested stable systems, in aggregate, and operating on time scales so much faster than their users, can be unstable and unpredictable. Cyber security of markets in aggregate is so complex that it has taken its place at the back of the queue. The stock market is only the most visible because of its high transparency. Resolution of issues in much larger markets, such as swaps (including what were once known as “toxic assets”) in the highly politicized SEF and FPML efforts, as we saw in 2008, can be more damaging than events in the stock market. An amazingly well written and detailed account of these issues in modern markets is found in Scott Paterson’s book “Dark Pools” which is about much more than dark pools in the narrow industry sense. At Leinweber & Co, our commercial work has been looking to extend quant methods into the “quanttextual” world, where information comes as “big data” in the form of words as well as numbers. Understanding complex evolving events are an area where humans still have game against computers. What do you see as academically exciting? I have a Higgs Boson in my sock drawer, but don’t tell. I also have a copy of Emanuel Derman’s rules for financial engineers to avoid bad or catastrophic model behavior. I think they should be tattooed on the inside of the eyelids of all financial engineers. What would you work on if you had lots of time? That is truly a tough question. In the book “How I Became a Quant” http://www.amazon.com/HowBecame-Quant-Insights-Streets/dp/0470050624 I ‘fessed up to being an “accidental quant”, where early work on real-time defense systems at RAND led to early “electronic order working”, which accelerated into algorithmic trading, and HFT (which has both white-hat and black-hat practitioners). In the 90s and early 2000s, I was pleased to tell my kid’s friends what I did for a living in electronic markets, and see from their reactions that I was doing something good. Since 2008, the sign on those reactions has flipped. At the end of the Manhattan Project, just before the test of the first atomic bomb, some physicists were worried that it might set the sky on fire. The non Wall Street twenty-somethings of today seem to feel that the misapplication of financial technology has set their future on fire. It doesn’t have to be this way. Derman’s credo of responsible
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来源期刊
Algorithmic Finance
Algorithmic Finance BUSINESS, FINANCE-
CiteScore
0.40
自引率
0.00%
发文量
6
期刊介绍: Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.
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