Strategy Trading---wiki

Trading strategy

In finance, a trading strategy (see also trading system) is a predefined set of rules for making trading decisions.

Traders, investment firms and fund managers use a trading strategy to help make wiser investment decisions and help eliminate the emotional aspect of trading. A trading strategy is governed by a set of rules that do not deviate. Emotional bias is eliminated because the systems operate within the parameters known by the trader. The parameters can be trusted based on historical analysis (backtesting) and real world market studies (forward testing), so that the trader can have confidence in the strategy and its operating characteristics.

Contents
1 Development
2 Executing strategies
3 Styles
4 Timeframes


Development When developing a trading strategy, many things must be considered: return, risk, volatility, timeframe, style, correlation with the markets, methods, etc. After developing a strategy, it can be backtested using computer programs. Although backtesting is no guarantee of future performance, it gives the trader confidence that the strategy has worked in the past. If the strategy is not over-optimized, data-mined, or based on random coincidences, it might have a good chance of working in the future.

Executing strategies A trading strategy can be executed by a trader (manually) or automated (by computer). Manual trading requires a great deal of skill and discipline. It is tempting for the trader to deviate from the strategy, which usually reduces its performance.

An automated trading strategy wraps trading formulas into automated order and execution systems. Advanced computer modeling techniques, combined with electronic access to world market data and information, enable traders using a trading strategy to have a unique market vantage point. A trading strategy can automate all or part of your investment portfolio. Computer trading models can be adjusted for either conservative or aggressive trading styles.

The publication of Trading Strategy Indices as investable indices that implement a range of trading strategies has become a growing business for many of the major Investment banks.

Styles
Mirror trading
Technical analysis
Fundamental analysis
Quantitative trading
Trend following
Mean reversion
Volatility (finance)
TradingLevels
Price action trading

Timeframes
Intraday
High-frequency
Scalping (trading)
Shaving (trading) - a round trip trade (a buy and sell) in as little as one second.
Momentum (finance)
Day trading
Trend following
Gorilla Trading - Buying near the closing bell and selling the next morning (holding a position overnight)
Long term trading
Short term trading
Trading Strategy Index

Strategy Planning--6C/3P/MBM

Strategy Investing--Smart Business Plan

XXX公司（或XXX项目）商业计划书 编号： 日期： （项目公司资料） 地址： 邮政编码： 联系人及职务： 电话： 传真： 网址／电子邮箱：
保 密
本商业计划书属商业机密，所有权属于XX公司（或XX项目持有人）。所涉及的内容和资料只限于已签署投资意向书的投资者使用。收到本计划书后，收件方应即刻确认，并遵守以下的规定：
1、在未取得XX公司（或XX项目持有人）的书面许可前，收件人不得将本计划书之内容 复制、泄露、散布；
2、收件人如无意进行本计划书所述之项目，请按上述地址尽快将本计划书完整退回。
目 录 报告目录 第一部分 摘要（整个计划的概括） （文字在2页～3页以内）
一、公司简单描述 二、公司的宗旨和目标（市场目标和财务目标） 三、公司目前股权结构 四、已投入的资金及用途 五、公司目前主要产品或服务介绍 六、市场概况和营销策略 七、主要业务部门及业绩简介 八、核心经营团队 九、公司优势说明 十、目前公司为实现目标的增资需求：原因、数量、方式、用途、偿还 十一、融资方案（资金筹措及投资方式） 十二、财务分析
1.财务历史数据（前3年～5年销售汇总、利润、成长）
2.财务预计（后3年～5年）
3.资产负债情况
第二部分 综述 第一章公司介绍 一、公司的宗旨（公司使命的表述） 二、公司简介资料 三、各部门职能和经营目标 四、公司管理
1.董事会
2.经营团队
3.外部支持（外聘人士／会计师事务所／律师事务所／顾问公司／技术支持／行业协 会等）
第二章技术与产品 一、技术描述及技术持有 二、产品状况
1.主要产品目录（分类、名称、规格、型号、价格等）
2.产品特性
3.正在开发／待开发产品简介
4.研发计划及时间表
5.知识产权策略
6.无形资产（商标／知识产权／专利等） 三、产品生产
1.资源及原材料供应
2.现有生产条件和生产能力
3.扩建设施、要求及成本，扩建后生产能力
4.原有主要设备及添置设备
5.产品标准、质检和生产成本控制
6.包装与储运
第三章市场分析 一、市场规模、市场结构与划分 二、目标市场的设定 三、产品消费群体、消费方式、消费习惯及影响市场的主要因素分析 四、目前公司产品市场状况，产品所处市场发展阶段（空白／新开发／高成长／成 熟／饱和），产品排名及品牌状况 五、市场趋势预测和市场机会 六、行业政策
第四章竞争分析 一、无行业垄断 二、从市场细分看竞争者市场份额 三、主要竞争对手情况：公司实力、产品情况（种类、价位、特点、包装、营销、市 场占有率等） 四、潜在竞争对手情况和市场变化分析 五、公司产品竞争优势
第五章市场营销 一、概述营销计划（区域、方式、渠道、预估目标、份额） 二、销售政策的制定（以往／现行／计划） 三、销售渠道、方式、行销环节和售后服务 四、主要业务关系状况（代理商／经销商／直销商／零售商／加盟者等），各级资格 认定标准及政策（销售量/回款期限／付款方式／应收账款／货运方式／折扣政 策等） 五、销售队伍情况及销售福利分配政策 六、促销和市场渗透（方式及安排、预算）
1.主要促销方式
2.广告／公关策略媒体评估 七、产品价格方案
1.定价依据和价格结构
2.影响价格变化的因素和对策 八、销售资料统计和销售纪录方式，销售周期的计算。 九、市场开发规划，销售目标（近期、中期），销售预估（3年～5年）销售额、占有 率及计算依据
第六章投资说明 一、资金需求说明（用量／期限） 二、资金使用计划及进度 三、投资形式（贷款／利率／利率支付条件／转股－普通股、优先股、任股权／对应
价格等） 四、资本结构 五、回报／偿还计划 六、资本原负债结构说明（每笔债务的时间／条件／抵押／利息等） 七、投资抵押（是否有抵押／抵押品价值及定价依据／定价凭证） 八、投资担保（是否有抵押／担保者财务报告） 九、吸纳投资后股权结构 十、股权成本 十一、投资者介入公司管理之程度说明 十二、报告（定期向投资者提供的报告和资金支出预算） 十三、杂费支付（是否支付中介人手续费）
第七章投资报酬与退出 一、股票上市 二、股权转让 三、股权回购 四、股利
第八章风险分析 一、资源（原材料／供应商）风险 二、市场不确定性风险 三、研发风险 四、生产不确定性风险 五、成本控制风险 六、竞争风险 七、政策风险 八、财政风险（应收账款／坏账） 九、管理风险（含人事／人员流动／关键雇员依赖） 十、破产风险
第九章管理 一、公司组织结构 二、管理制度及劳动合同 三、人事计划（配备／招聘／培训／考核） 四、薪资、福利方案 五、股权分配和认股计划
第十章经营预测 增资后3年～5年公司销售数量、销售额、毛利率、成长率、投资报酬率预估及计 算依据
第十一章财务分析 一、财务分析说明 二、财务数据预测
1.销售收入明细表
2.成本费用明细表
3.薪金水平明细表
4.固定资产明细表
5.资产负债表
6.利润及分配明细表
7.现金流量表
8.财务指标分析 （1）反映财务盈利能力的指标
a.财务内部收益率（FIRR）
b.投资回收期（PT）
c.财务净现值（FNPV）
d.投资利润率
e.投资利税率
f.资本金利润率
g.不确定性分析：盈亏平衡分析、敏感性分析、概率分析
(2)反映项目清偿能力的指标
a.资产负债率
b.流动比率
c.流动比率
d.固定资产投资借款偿还期
第三部分 附录 一、附件
1.营业执照影印本
2.董事会名单及简历
3.主要经营团队名单及简历
4.专业术语说明
5.专利证书／生产许可证／鉴定证书等
6.注册商标
7.企业形象设计／宣传资料（标识设计、说明书、出版物、包装说明等）
8.简报及报道
9.场地租用证明
10.工艺流程图
11.产品市场成长预测图 二、附表
1.主要产品目录
2.主要客户名单
3.主要供货商及经销商名单
4.主要设备清单
5.主场调查表
6.预估分析表
7.各种财务报表及财务预估表

Strategy Investing--Smart LEGO with FUN!

http://aboutus.lego.com/en-US/factsfigures/default.aspx

Strategy Planning--Holistic Enterprise Strategy Planning

企業整體策略規劃與執行
-公司定位, 競爭優勢與策略, 企業6C
-市場新訊及產業趨勢報告
-標竿公司典範學習策略
-PDCAB持續改善, SAPIM異常管理
-成長策略
-產品與技術發展策略
-產品發展藍圖
-策略聯盟, 投資, 整合, 整併
-新事業發展與技術團隊引進
-營運策略與執行
-政府獎助與獎項申辦
-主導性計劃申辦(補助>1000萬)
-科園區分公司申辦進駐
-技術顧問引進
-年度營運報告與褐皮書

Strategy Investing--Smartness

Bookmarks

1. Smart healthcare

cerner.com Home.url

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Mesa Laboratories, Inc. I Data Loggers - Dialysate Meters - Biological Indicators - Torque Testing - Validation Services.url

Quality Systems, Inc..url

Revenue cycle, physician performance, cost management Advisory Board Company—Peformance Technology.url

smart balance - Google 搜尋.url

 CSD WebClient - Cegedim Strategic Data - Integrated Healthcare Research.url

護理資訊系統 - Cornerstone for eHealth.url

鉅貫德科技股份有限公司 - 創新企業雲端服務方案.url

首頁│數位通國際eASPNet，台灣第一個電信級的網際網路資料中心及應用服務供應商.url

2. Financial Operation

CMoney法人決策分析輔助系統 - 創新理念與技術.url

Yasser Soft 雅策資訊 - 期指晨星、程式交易、績效評比、自動化交易、財金論壇、程式交易教學、策略代工研發.url

‧ 三 竹 資 訊 首 頁‧.url

全曜財經資訊股份有限公司.url

商智資訊-全方位財富管理系統.url

嘉實資訊==全球華人金融資訊提供者==.url

 

3. Mobile platform kits

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優必達科技有限公司.url

創易通科技股份有限公司 - EzMoBo Corp..url

春水堂科技娛樂股份有限公司.url

4. GIS

AsiaTEK-華夏科技衛星定位通訊車機、ITS系統解決專家.url

Sonocaddie, Golf GPS devices with 3D Color Course Layout.url

WaveGIS 昕傳科技 - 行動科技的領航者.url

康訊科技股份有限公司-首頁.url

5. Content & content search smartness

airiti華藝數位 - Airiti Library內容特色.url

Artkey--艺术授权,台北故宫,富春山居.url

Gorilla Technology About Gorilla.url

Wisers Information Limited.url

WordPedia 智慧藏百科網 - ONE-STOP Knowledge.url

慧科訊業.url

解決方案 龍捲風科技 Tornado Tech.url

鍵承科技股份有限公司 KeyChamp.url

6. Smart mobility

Groundhog Technologies Inc..url

Taisys.url

7. Smart training

Renaissance Learning - Advanced Technology for Data-Driven Schools.url

TeamWell群崴管理.url

TKB臺灣知識庫-全國最大知識學習網 提供升學考試,公職考試,高普初考,各類特考,證照執照,終身學習,數位學習,線上購課全方位學習加值服務.url

鍵承科技股份有限公司 KeyChamp.url

隱陸達通 IVICON ASIA 知識經濟 優質教育.url

8. Smart advertising

CCMedia Technology Company.url

Vpon行動廣告聯播.url

宇匯知識科技.url

歡迎來到生動科技.url

9. Smart e-business

Flowring 台灣.url

Welcome To eBsuccess!!.url

功典資訊MIGOSOFT-- CRM-eMarketing Expert.url

協同商務解決方案領導供應商-聯合通商電子商務.url

紅陽科技股份有限公司-線上金流服務.url

10. Smart clouding

11. Smart homing

KRC 新鎧科技 - 一切都發生在網路上.url

台灣PORIS 首頁—門禁保全系統 大樓自動化產品廠商.url

奇想創造 GIXIA GROUP.url

展望國際股份有限公司.url

12. Smart energy, water....... & use

能源資通訊專家 Energy Infocom Technology Expert - 縱橫資通能源股份有限公司.url

開創水資源科技股份有限公司.url

13. e-security

esecure.url

本網頁施工中.url

源碼檢測與 Web 應用程式安全 - Armorize Technologies Inc..url

關於阿碼科技 Web 應用程式安全軟體的開發與研究 - Armorize Technologies Inc..url

騰泰科技股份有限公司.url

14. Smart fab

三和技研股份有限公司 Sanwa Engineering Corp..url

先知科技股份有限公司.url

創益科技顧問股份有限公司.url

無塵室、空調、機電工程整合歡迎光臨-聖暉工程.url

15. Smart exercise

TONIC FITNESS TECHNOLOGY.INC.url

16. Smart robot

Raycop紫外線除蟎(雷剋螨)殺菌吸塵器--鑫承智慧科技股份有限公司：紫外線吸塵器(Raycop)第一品牌.url

17. Smart school

天方科技股份有限公司.url

18. Smart material

Coremax - About.url

Google.url

High-IQ TM - Intelligent effects branding scheme by Huntsman International LLC..url

允拓材料科技-LINK UPON ADVANCED MATERIAL CORP..url

導電, 導熱, 抗靜電,俊馳材料科技股份有限公司,.url

引擎觸媒還原劑(添藍) - 商品沿革 - 諾瓦材料科技股份有限公司 NOVAX Material & Technology Inc..url

昌鴻興業.url

紡合實業 FLYCOOL.url

肯美特材料科技股份有限公司 Chem-Mat Technologies CO., LTD..url

19.Smart career planning & life

104人力銀行~不只找工作，為你找方向！不只找人才，為你找夥伴！.url

20. Smart tool

Moldex3D - True 3D CAE for Injection Molding - 首頁.url

盛鑫軟體 PCB軟體.url

21. Smart shopping

首頁 - lativ 國民服飾.url

 

Strategy Trading---Benchmarking James Simons II

Jim Simons - Renaissance Technologies Corp
Interviews

James Harris (Jim) Simons, Ph.D. founded Renaissance Technologies Corporation in 1982. Jim Simons is also involved the discovery and application of certain geometric measurements, and resulted in the Chern-Simons theory.  As a hedge fund manager, his trading philosophies involved exploiting inefficiencies in different markets and profit from them.
Below is an interview with Jim Simmons conducted in November 2000.
The Secret World of Jim Simons
How does this prize-winning mathematician and former code breaker rack up his astonishing returns?
By Hal Lux
November 2000
How does this prize-winning mathematician and former code breaker rack up his astonishing returns? Try a little luck-- and a firm full of Ph.D.s.

Last April the State University of New York at Stony Brook held a gala reception at the Waldorf-Astoria Hotel in midtown Manhattan to celebrate raising a record $1 million -- a tidy sum for a state school. After cocktails a balding, white-haired man rose from his seat on the dais to thank the sellout crowd, which included such celebrities as Oscar-winning movie director Martin Scorsese, for its generosity.

"I told my wife, 'We raised $1 million for Stony Brook,'" said the speaker, hedge fund manager James Simons. "She said, 'Gross or net?'"

Chances are you haven't heard of Jim Simons, which is just fine by him. Nor are you alone. Many on Wall Street, including competitors in his specialty, quantitative trading, haven't heard of Simons or of his operation, Renaissance Technologies Corp., either. And that's simply extraordinary -- because, gross or net, Simons may very well be the best money manager on earth.

An extreme judgment? Perhaps. Certainly, there has been no end of claimants to the title. And one after another, over the past few years, these celebrated managers have either blown up or folded their tents. After big reverses, Julian Robertson closed down Tiger Management, and George Soros scaled back the activities of his Quantum Fund this year. John Meriwether's Long-Term Capital Management nearly took down the financial world in 1998.

Simons, by contrast, just keeps getting better. Consider his performance over the past decade. Since its inception in March 1988, Simons' flagship $3.3 billion Medallion fund, has amassed annual returns of 35.6 percent, compared with 17.9 percent for the Standard & Poor's 500 index. For the 11 full years ended December 1999, Medallion's cumulative returns are an eye-popping 2,478.6 percent. Among all offshore funds over that same period, according to the database run by veteran hedge fund observer Antoine Bernheim, the next-best performer was Soros' Quantum Fund, with a 1,710.1 percent return.

"Simons is No. 1," says Bernheim. "Ahead of George Soros. Ahead of Mark Kingdon. Ahead of Bruce Kovner. Ahead of Monroe Trout."

And Bernheim's numbers don't include Medallion's 2000 performance. In a year of exceptional volatility and market dislocations, the fund is up 64 percent through September. Over the years, Simons' consistency has been exceptional. Apart from his second year, 1989, his fund has not had a losing year (it was down 4.1 percent that year). In fact, in the past decade, it's never returned less than 21 percent.

"Ten years ago I put a small amount of money into Medallion," says one pleased investor, Richard Gelfond, the co-CEO of Imax Corp., the Canadian giant-screen film company. "Today it's a big amount of money."

Medallion, which closed to new investors in 1993, is focused chiefly on commodities and futures trading. Recently, Simons has expanded his equity business. Last year he launched Equimetrics, a $500 million U.S. fund with a market-neutral trading strategy for institutional investors. Despite market ructions, and the first declining U.S. stock prices in years, Equimetrics this year has returned 24.1 percent through September, compared with ­2.23 for the S&P 500 with two thirds the volatility.

And these are all, it should be noted, net numbers. The price of Simons' success is high for investors. He charges a management fee of a stunning 5 percent of assets, in addition to the normal hedge fund rake-in of 20 percent of profits.

To be sure, some investors have had even higher returns in recent years. Hedge fund manager Jeffrey Vinik closed his fund last month after compiling average annual returns of 53 percent since November 1996. And Steven Cohen of SAC Capital Management, reportedly posted returns of 70 percent last year and 49 percent the previous year. Simons, however, has made steady profits over 11 years, compared with just seven for Cohen and four for Vinik.

Simons' risk-adjusted returns are even more impressive. Paul Wick, manager of Seligman Communications and Information Fund, leads all U.S. mutual fund managers, according to Morningstar, with annual returns of 31 percent since 1990. But his Sharpe ratio over the past three years is 0.42; for the same period, Legg Mason's celebrated William Miller III boasts average annual returns of 24 percent -- and a Sharpe ratio of 0.64. Simons wracked up a ten-year Sharpe ratio of 1.89 throughout the 1990s, with a 2.52 ratio for the last five years of the decade. Sharpe ratios are a measure of risk-adjusted returns. The higher the number, the better.

How does Simons do it? Start with a world-class mathematical mind. In 1976, at 38, Simons won the American Mathematics Society's Veblen Prize -- awarded every five years, it is the geometry world's highest honor -- for his work in the excruciatingly esoteric field of differential geometry. His signature work -- a 26-year-old theorem crafted with renowned geometrician Shiing-Shen Chern that is known as the Chern-Simons theory -- has recently emerged as a critical tool for theoretical physicists searching for fundamental laws of the universe. "Chern-Simons pervades a whole class of theories that underlie our fundamental view of the observable world," says Brandeis University physicist Stanley Deser, an expert on supergravity, a discipline of quantum theory that studies elementary particles and their interaction.

"Jim Simons is without question one of the really brilliant people working in this business," says quantitative trading star David Shaw, chairman of D.E. Shaw, which boasts returns above 50 percent this year. "He is a first-rate scholar, with a genuinely scientific approach to trading. There are very few people like him."

Simons surrounds himself with like minds. The headquarters of Renaissance, in the quaint town of East Setauket on New York's Long Island, resembles nothing so much as a high-powered think tank or graduate school in math and science. Operating out of a one-story wood-and-glass compound near SUNY Stony Brook, Renaissance, founded in 1982, has 140 employees, one third of whom hold Ph.D.s in hard sciences. Many have studied or taught in Stony Brook's math department, which Simons chaired from 1968 to 1976. Among their ranks: practitioners in the fields of astrophysics, number theory, computer science and computational linguistics. In notably short supply are finance types. Just two employees, including the head of trading, are Wall Street veterans.

"I have one guy who has a Ph.D. in finance. We don't hire people from business schools. We don't hire people from Wall Street," says Simons. "We hire people who have done good science."

Confident and witty but intensely secretive about his business's inner workings, Simons shuns publicity. He agreed to talk with Institutional Investor only after much pestering. And some of what he said was, frankly, unintelligible. We made the mistake of asking him to explain Chern-Simons. After half an hour he allowed, "I can't." He meant, of course, to us.

Simons rarely speaks at financial forums, preferring math conferences. He celebrated his 60th birthday with a geometry symposium at Stony Brook that included such lectures as "Generalized Chern-Simons Invariants as a Generalized Lagrangian Field Theory." That's one reason he is little known on Wall Street. Two years ago Renaissance invited Andrew Lo, whose financial engineering program at the Massachusetts Institute of Technology is the prime recruiting ground for quantitative traders, to speak at its headquarters on options replication. "I had heard of Jim Simons the mathematician, but I had never heard of Renaissance until they called me up," says Lo. "I said, 'Jim Simons runs a hedge fund?'"

When he does open up, Simons can seem exasperatingly coy in describing his success. "Luck," he told a gathering of potential investors last spring in Greenwich, Connecticut, "is largely responsible for my reputation for genius. I don't walk into the office in the morning and say, 'Am I smart today?' I walk in and wonder, 'Am I lucky today?'"

In fact, Simons is being straightforward. Luck may be the residue of design to baseball minds, but to a mathematician it's the twin of probability, which can be approached through statistical studies. Renaissance's researchers construct statistical models and proprietary algorithms from exhaustive scrutiny of market data.

Like all quantitative money managers, Renaissance aims to find small market anomalies and inefficiencies that can support profitable trading on billions of dollars of capital. Though all quant shops are alike in their dedication to models -- Let the best algorithm win! -- Renaissance's approach differs from the "convergence trading" popularized by John Meriwether's Long-Term Capital Management and similar arbitrage shops. Convergence traders price financial instruments based on complex mathematical models, find two different instruments that are cheap and expensive on a relative basis and then buy one and sell the other, betting that the prices will, at some point, have to return to their proper level. The Renaissance approach requires that trades pay off in a limited, specified time frame. And Renaissance traders never override the models.

Guided by these models, Medallion's 20 traders conduct rapid-fire buying and selling of a multitude of U.S. and overseas futures contracts, including all major physical commodities, financial instruments and important currencies, in addition to trading equities and mortgage derivatives. This year Medallion made a killing in the volatile oil futures market.

To be sure, Simons' track record is not unblemished. In 1997 he folded a middling market-neutral fund into Medallion after just three years. And a mortgage-backed-derivatives fund he backed in 1995 swooned after enjoying two fine years.

An active venture capitalist and private equity investor in the U.S. and Latin America, Simons sits on the boards of four companies, including Franklin Electronic Publishers, the pioneering electronic spell-checker and book company, of which he owns 22 percent and is chairman. Simons has recently raised a $200 million fund to extend Renaissance's reach into technology venture capital investments.

Simons' trading record over the past decade is more than luck. The bigger question is whether he can keep it up. A chain-smoker in defiance of statistical possibilities, Simons is 62 and has no designated successor, and the firm is starting to expand into new areas.

Such a situation can be a recipe for disaster for trading firms. But Simons says with scientific certainty: "The things we are doing will not go away. We may have bad years, we may have a terrible year sometimes. But the principles we've discovered are valid."

THE SON OF A SHOE FACTORY OWNER, JIM Simons grew up daydreaming about numbers. "I wanted to do mathematics from the time I was 3," says Simons, who was raised in the Boston suburb of Newton. "Literally. I would think about numbers and shapes."

After graduating from Newton High School, he entered MIT, studying under renowned mathematicians Warren Ambrose and I.M. Singer. (Says Singer: "He's very intuitive. He has a sense of taste for the right principles in mathematics, and that is very rare, let me tell you.") He received his BS in math in 1958 at 20 and a mere three years later, a Ph.D. in math from the University of California at Berkeley. By 23 he was back at MIT -- on the faculty. After one year, he strolled up Massachusetts Avenue and spent two more years as a math professor at Harvard University. There he worked on solutions to such conundrums as the plateau problem and the Bernstein conjecture, which grapple with the properties of multidimensional surfaces.

Though a rising star in his field, Simons quickly tired of academic life. Seeking adventure, he signed on in 1964 as a code breaker with the Institute for Defense Analyses, a nonprofit research organization that performed work for the U.S. Department of Defense. Angered by a New York Times Magazine story that he thought overly optimistic about the military effort in Vietnam, Simons made comments to Newsweek that were critical of the war. After telling his boss about the interview, he says he was fired from IDA.

Shaken, Simons quickly found a home back in academia. He took the post of chairman of the Stony Brook math department, where he would spend the next eight years doing pure research. "I felt so powerless about being fired," he says. "I thought, 'They can't fire you if you're chairman.'"

Simons' most famous work is his 1974 paper "Characteristic Forms and Geometric Invariants," which he coauthored with the renowned Berkeley geometer Chern. It represented an important breakthrough in geometry that would become known as the Chern-Simons theory.

Differential geometry, Simons' specialty, is the study of curved surfaces and spaces. We are all familiar with spheres; we live on a very big one, after all. But it turns out that, for mathematicians, they are fiendishly complicated. Geometers establish the properties that separate one type of object from another. Although they have been able to determine the properties of what to laymen are such incomprehensible objects as spheres with ten dimensions, they have not been able to do this with spheres in the third dimension. French mathematician Henri Poincaré postulated at the beginning of this century that they ought to be able to do so using simple means. Like Fermat's last theorem, which was finally proved in 1993 after centuries of fruitless efforts, Poincaré's conjecture is one of the mathematical world's great remaining mysteries. (The Clay Mathematics Institute has offered a $1 million prize to anyone who can find the solution.)

Chern-Simons was not meant to solve Poincaré's conjecture, but it does offer calculations that are useful for distinguishing among shapes in three dimensions. "Chern-Simons offers a route to solve Poincaré's conjecture," says Simons, "but it's a difficult route, and there are other difficult routes."

Difficult, yes, but, say mathematicians, an elegant piece of abstract reasoning. "Chern-Simons, that's a beautiful thing," says George Zettler, a former Columbia University math professor, who now trades swaps and options for the mortgage-backed-securities hedge fund Ellington Management Group. But the paper's language defies translation into plain English. A random sample: "The Weil homomorphism is a mapping from the ring of invariant polynomials of the Lie algebra of a Lie group, G, into the real characteristic cohomology ring of the base space of a principal G-bundle." Difficult, indeed.

Chern-Simons has taken on a second intellectual life, because the theory has come to have a major influence in a completely different scientific discipline. In the mid-1980s Princeton University professor Edward Witten, now one of the world's leading theoretical physicists, noticed the applicability of Chern-Simons to physics and popularized an area that is loosely called Chern-Simons quantum field theory. These days Chern-Simons is used as a tool in many areas of physics research, from string theory to supergravity to black holes. "Every day a physicist is working on a new theory with Chern-Simons," notes Dennis McLaughlin, a Princeton-mathematician-turned-McKinsey-&-Co.-consultant.

NOT ONE TO REMAIN LOST IN ABSTRACTIONS,
Simons has long had an affinity for business. In 1961, he and a few MIT classmates invested in a Colombian floor tile and pipe company. At Berkeley he tried his hand at trading, looking to invest about $5,000 in wedding gifts from his first marriage. He found that stocks bored him. "I went to a Merrill Lynch broker," recalls Simons. "He said, 'Try soybeans.'"

Simons didn't get really hooked on trading until the early 1970s, when he was at Stony Brook. In 1973 the tile company got sold, and he turned the proceeds over to a mathematician he knew who was trading commodities. "In eight months he had multiplied my money by ten times," says Simons.

Even as he was being hailed for his theoretical work, Simons began to make the transition out of academics, working half time at Stony Brook and trading currencies with his own money between 1976 and 1978. In 1978 he left Stony Brook completely, to form a private investment fund called Limroy. Initially, he took a fundamental approach, trying to predict factors like Federal Reserve Board policy and interest rate movements. Over the next ten years, Limroy grew initial capital 25 times by investing in everything from venture capital to technical currency trading.

In 1988 Simons decided to launch a fund that concentrated on pure trading. He shut Limroy and launched Medallion in March 1988. Concentrating on futures trading, the fund earned 8.8 percent in 1988 but lost money steadily in 1989 until Simons halted trading in June.

For six months Simons and former Princeton mathematician Henry Laufer, who is still Renaissance's research chief, rebuilt Medallion's trading strategy, shifting from fundamental analysis to the quantitative approach that powers the firm today. "We started to think about a whole new way to look at futures," says Simons.

Back in action, Medallion made its mark through rapid, short-term trading across futures markets. In the early years the types of inefficiencies that could be exploited by quantitative trading abounded. The firm made money by simply arbitraging Treasury bills against Treasury futures contracts. Luck helped too. In 1990, for example, the fund was long oil when Iraq invaded Kuwait.

Simons steadily recruited top-tier scientists. They focused on speeding up systems, studying how to optimize risk allocation and determining trading strategies. By 1993, after three dazzling years, Medallion had reached $270 million in assets and stopped taking new money. And Simons began extending his reach. By 1994 Renaissance, which had started with 12 employees, had 36 on staff, and Medallion was trading 40 types of securities, up from 12. Renaissance had always done all of its trading through outside brokers; the following year it opened its first in-house trading operation. Today Renaissance has 140 staffers -- it plans to be up to 150 by year-end -- and trades 60 different financial instruments around the clock.

"We have three criteria," says Simons. "If it's publicly traded, liquid and amenable to modeling, we trade it."

Three years ago Medallion formed an internal fund-of-funds to invest in outside managers. In part, the fund was looking for new ways to invest excess capital that investors didn't want back. Simons also believed the approach would increase Renaissance's market intelligence and occasionally present opportunities for Renaissance to acquire another fund. Medallion now has $500 million invested in 40 outside funds, including macro manager Louis Bacon's Moore Capital Management.

Expansion doesn't always work, however. Medallion began trading mortgage derivatives in its fixed-income portfolio in 1992. Though not a Renaissance employee, ex­Lehman Brothers mortgage trader Judah Frankel managed the portfolio for Medallion. In 1995, following the 1994 bond market rout, Renaissance decided to make a larger commitment to the mortgage market and became a co­general partner in a new hedge fund called Matrix, run by Frankel. After gaining 27.4 percent in 1995, the fund racked up a stellar 101.3 percent return in 1996, according to hedgefundnews.com. Then interest rates moved out of the range projected for many of Matrix's trades, and the yield curve inverted. In 1997 the fund gained just 3.3 percent; then in 1998 it lost 20.6 percent of assets. Though several Renaissance executives still have money in Matrix, the company withdrew as a general partner last year. "Inversion was the kiss of death," says Simons. "The fund was unhedged with respect to the yield curve."

Renaissance, he says, would not play such a prominent role in someone else's fund again.

SINCE THE ADVENT OF THE OPTIONS AND
derivatives industry in the early 1980s, Wall Street's banking houses have fallen over themselves to recruit high-powered intellectuals from academia. By contrast, Simons has taken his scientists away from Wall Street, to lavish surroundings in Long Island, not far from the Stony Brook campus where Renaissance began life in office space designed to serve as a business incubator.

Renaissance moved into its East Setauket headquarters three years ago, but Simons and the firm retain close ties to the university. Thanks to Simons' generosity, Stony Brook is considered one of the top ten math departments in the country; several math professors hold the title of James Simons Instructor. In addition to having led for more than a decade the Stony Brook Foundation, which raises and invests a private endowment for the school, Simons has helped the school take a lead role in assuming the management of Brookhaven National Laboratory for the Department of Energy.

Renaissance headquarters feature a gym, lighted tennis courts, a library with a fireplace and large private offices for every employee. All back-office and administrative functions are handled out of the firm's New York offices.

Unusual for a hedge fund, the heart of Renaissance is not its trading room -- an uncluttered room where a score of traders buy and sell around the clock -- but rather an auditorium with exposed beams that seats 100 and features biweekly science lectures. Last month a molecular biologist presented research on colon cancer. "When you hear someone talk about an interesting use of statistics it helps trigger your thinking," says one Renaissance employee.

The atmosphere is college casual, if intense -- think of a perpetual exam week. Though a natty dresser, Simons sets a properly idiosyncratic tone. "He has been known to show up at formal business meetings without socks," says Jerome Swartz, Simons' next-door neighbor on Long Island and an Equimetrics investor.

Married to his second wife, Marilyn, for about 25 years, Simons has four children, two of whom are still school age. At Renaissance he works out of a tidy office with fashionable leather furniture and a large, somewhat gruesome painting of a lynx killing a rabbit. "I used to have it in my house," says Simons. "My wife didn't particularly like it."

Staff turnover is nearly nonexistent. Every six months all employees receive cash bonuses based on fund performance. The six-month benchmark is said to be 12 percent -- and it's almost always easily surpassed. Most employees also hold equity in the firm. Simons frequently takes the entire staff and their families -- more than 300 people -- on lavish weekend vacations. Earlier this year he flew everyone to Bermuda.

Renaissance is divided into three basic groups: computer and systems specialists, researchers and traders. Once a week Simons meets with the research group, discussing in detail the progress of trading strategies under development.

Job candidates don't have to know any finance -- in fact, Wall Street experience is a black mark -- but they must present a talk on their scientific research to the entire firm before being offered a job. Most staffers seem to know little about the rest of the financial services industry, or even the hedge fund business. Asked about the performance of legendary futures trader and Renaissance rival Paul Tudor Jones, one researcher says, "Who's Tudor Jones?"

For a man who believes in luck, Simons doesn't leave a lot to chance when it comes to recruiting the staff that builds his trading models. As the firm's assets grew, Simons recruited top-flight mathematicians and scientists, including University of Virginia physics professor Robert Lourie and Bell Labs numbers theorist Peter Weinberger, to research new trading strategies. In recent years Simons seems to be especially keen on stockpiling computational linguists who have worked on building computers that can recognize speech. He has hired away a good part of the speech recognition group from IBM Corp.

Why computational linguists? "Investing and speech recognition are very similar," says one Renaissance researcher. "In both, you're trying to guess the next thing that happens."

AS A TRADER, SIMONS TRIES TO OVERCOME
fundamental laws, not discover them. In the case of quantitative finance, the law is the efficient-markets hypothesis and the belief that markets should be difficult, but not impossible, to beat.

In his rare discussions of trading, the Renaissance president emphasizes that trading opportunities are by their nature small and fleeting. "Efficient market theory is correct in that there are no gross inefficiencies," Simons told the Greenwich Roundtable last year. "But we look at anomalies that may be small in size and brief in time. We make our forecast. Then, shortly thereafter, we reevaluate the situation and revise our forecast and our portfolio. We do this all day long. We're always in and out and out and in. So we're dependent on activity to make money."

Renaissance essentially attempts to predict the future movement of financial instruments, within a specific time frame, using statistical models. The firm searches for something that might be producing anomalies in price movements that can be exploited. At Renaissance they're called "signals." The firm builds trading models that fit the data.

When the trading starts, the models run the show. Renaissance has 20 traders who execute at the lowest cost and without moving markets, crucial requirements for quant investors trading on narrow margins. But the models decide what to buy and sell. Only in cases of extreme volatility, or if the signals appear to be weakening, does the firm sometimes manually cut back. Says Simons, "We don't override the models."

Even in structuring its hedge fund-of-funds portfolio, Medallion takes a quantitative approach. The fund balances the positions of its outside portfolio to ensure that, overall, the fund has no stock market exposure; it is, in other words, a "beta zero" portfolio. Last year the fund-of-funds, on a risk-adjusted basis, even beat trading returns, posting a higher Sharpe ratio than the 2.31 recorded for the overall fund and accounting for about 7 percent of the Medallion's revenues. Nevertheless, investing with outside managers poses certain challenges. "We treat these funds as instruments," says Simons. "But unlike the deutsche mark, managers change their character over time. It's messier to model those time series, but it's not impossible. We do our best."

Renaissance has also pioneered advanced trading technologies that make it possible to earn money on small margins. When few firms were thinking about electronic trading, a Renaissance subsidiary quietly installed a direct trading link to the German futures exchange. "The world is moving in our direction," says one Renaissance executive. "If the NYSE went all electronic it would be great for us."

Though Simons won't reveal the specifics of his trading, it's possible to get a glimpse of Renaissance's style by looking at Equimetrics, the U.S. market-neutral, long-short portfolio, started in April 1999 partly to expand Renaissance's base of institutional investors. Where Renaissance's traditional strength is rapid trading, Equimetrics hopes to apply the same principles to low-turnover trading.

Equimetrics was developed by Robert Frey, its CEO, an eight-year Renaissance veteran who previously worked in the secretive Morgan Stanley & Co. analytical proprietary trading group. All trades in the Equimetrics portfolio are made strictly from proprietary, computer-model-driven strategies, which pick from a universe of about 1,500 highly liquid common and preferred stocks. Typically, the portfolio holds about 1,000 positions, with stock index futures used to adjust the overall risk. No stock is expected to account for more than 5 percent of the portfolio, which will turn over only one to three times per year. The portfolio's leverage is a modest 2- or 3-to-1. (At the end of the second quarter, Renaissance held stock positions of about $2 billion in all its funds, according to Securities and Exchange Commission filings.)

So far the strategy is working. Last year, in nine months of trading after the fund's April 1999 launch, Equimetrics gained 12.1 percent, compared with 14.2 percent for the S&P 500. But this year, with the index down 2.23 percent through September, the fund was up 24.1 percent; the volatility of the fund has been just two thirds that of the index.

An Equimetrics report issued to investors in August, shows the nature of some holdings. As the S&P 500 climbed 6.2 percent and Nasdaq rose 11.7 percent for the month, Equimetrics was up 4.4 percent, holding a portfolio with its highest sector weightings in technology (17 percent long, 14 percent short); industrials (5 percent long, 3 percent short); and energy (9 percent long, 7 percent short). The portfolio's short positions had a high price-earnings ratio, 26.7, compared with 18.6 on the long side, and long positions were focused on companies with much higher average market capitalizations -- $35.8 billion -- compared with an average $19.6 billion for the shorts.

All sectors had a net exposure of 2 percent or less, except consumer noncyclicals, which had a ­6 percent exposure. "As market indices soared this month, the short positions took losses, but Equimetrics' long portfolio generated strong returns particularly in technology, financial and energy stocks," notes the Equimetrics report to investors.

What is not known are the secrets of the algorithms that can pick stocks smartly enough to beat the market with a portfolio that's short and long and trade efficiently enough to hold down costs to a bare minimum. Simons explains his firm's approach as the financial econometrics equivalent of blocking and tackling. "We search through historical data looking for anomalous patterns that we would not expect to occur at random. Our scheme is to analyze data and markets to test for statistical significance and consistency over time," says Simons. "Once we find one, we test it for statistical significance and consistency over time. After we determine its validity, we ask, 'Does this correspond to some aspect of behavior that seems reasonable?'"

RENAISSANCE'S RAPID GROWTH, AND ITS continued diversification into new markets, creates enormous risks for the firm. Even as it grows its core Medallion business, Renaissance is trying to master new, difficult areas, from venture capital to low-turnover trading to investing in outside managers. All have produced blowups at other successful investment firms; some rely on talents far afield from Renaissance's scientific focus.

His firm, insists Simons, remains squarely focused on scientific finance. "I don't think we would do well getting off of that stuff," he says.

Simons, however, will no longer be chief scientist. He's contemplating retirement in three to four years. Going emeritus, so to speak. He plans to indulge in some "old guy" stuff like traveling.

And then there's math. Last year Simons and his old college professor I.M. Singer started fooling around with a fiendishly involved problem. Both are too busy to plug away consistently, but when they get together, says Singer, the ideas start flying.

"It's a fundamental problem concerning the interaction between math and physics," says Singer. "He could possibly make a very serious contribution. I have been urging him to come back."

It's doubtful his investors will be so eager to lose him to the world of theory. i

The further ventures of Jim Simons

Renaissance Technologies fund whiz James Simons first traded stocks in the early 1960s, while in graduate school at the University of California at Berkeley. Soon after, he had tried his hand at venture capital, when, with some college buddies from the Massachusetts Institute of Technology, he invested in a Colombian floor tile and pipe company. That lucrative deal eventually gave him the capital he used to go into trading full time in the late 1970s.

Otherwise, though Simons remains an active venture capitalist, his track record is decidedly more mixed than his stellar trading history. Still active in Latin America as an investor in the Sanford Group, the industrial holding company that grew out of the investments he and his MIT classmates made, Simons visits the region twice a year.

But technology nowadays plays a prominent part in his U.S. portfolio. Simons got involved in the early personal computer, technology-gadgets and electronic-book markets through a 1981 investment in Franklin Computer Corp., which was founded that year as one of the original general purpose personal computer companies. But in 1984 the company filed for bankruptcy after being forced to settle a copyright infringement lawsuit brought by Apple Computer. It emerged from bankruptcy the following year under new management.

Through another company, Simons had helped to develop new technology that would give Franklin a second life. In 1979 Simons and scientist Peter Yianilos, an expert in artificial intelligence and speech recognition, had founded Proximity Technology, a pioneer in hand-held electronic book technology and spell-check software. The technology was futuristic. "The first book cost $800 to produce," recalls Yianilos.

Franklin bought Proximity in 1988, a year after Proximity had helped it develop the first blockbuster hand-held computer, a $69.95 spell-checker called Spelling Ace. Franklin's shares jumped from from 21/8 to more than 10. In 1990 the company was renamed Franklin Electronic Publishers. But despite such product innovations as electronic bibles and wine guides in the 1990s, the company's shares languished, with the exception of a brief run-up to 44 in 1995 when the company came out with one of the first electronic books. In October the stock was trading at 101/4, up from a 52-week low of 33/4.

Through an offshore trust, Simons, now chairman of New York Stock Exchange­listed Franklin, owns 22 percent of the company, a stake worth about $16 million. This fall the company is releasing a new multimedia device called eBookman, which will allow users to read and listen to books downloaded from the Internet. "Whether Franklin will someday be a huge success, I don't know," shrugs Simons.

A bigger score came in the late 1980s, when Simons invested in Numar Corp., a traditional oil services company, which had become a leader in applying magnetic resonance imaging technology to oil and gas exploration. Numar went public at 121/2 per share in April 1994 and was sold in 1997 to energy services and construction company Halliburton Co. Halliburton bought Numar for $430 million, making Simons' 900,000 shares of stock worth about $45 million, four times what they were worth at the time of the IPO.

Through his partnership in an investment firm called Long Island Venture Fund, Simons is a major shareholder in a dot-com direct marketing operation called MyPoints.com, which was taken public in August 1999 by Robertson Stephens. Simons now has a chance to feel dot-com pain, with the stock trading at 21/2, down 97.5 percent from its 52-week high of 9711/16. -- H.L.

The plane truth about trading from Simons

A theoretical-mathematician-turned-hedge-fund-manager-and-venture-capitalist, Renaissance Technologies founder and president James Simons has left his mark on fields ranging from futures trading to electronic books to theoretical physics. Publicity shy, Simons won't reveal any of the specific trading strategies that have allowed him to post one of the great long-term investing records of all times. But in a recent day of interviews with Senior Editor Hal Lux, Simons talked about his various academic and professional lives, what they have in common and what they don't.

Do you still do any math research?

Simons: I think about math, but not with any particular success. When I left academia, there were still a couple of problems I was interested in that I'd like to work on when I retire. About a year ago I started doing some work with a professor at MIT. I don't know that I have the brains for it anymore. This work is really different from the deep thinking you do in math.

Is there a connection between the math you did and your trading?

None. Absolutely none.

Yet you hire mathematicians and scientists to do much of your work. Why is that?

Mathematics and science are two different notions, two different disciplines. By its nature, good mathematics is quite intuitive. Experimental science doesn't really work that way. Intuition is important. Making guesses is important. Thinking about the right experiments is important. But it's a little more broad and a little less deep. So the mathematics we use here can be sophisticated. But that's not really the point. We don't use very, very deep stuff. Certain of our statistical approaches can be very sophisticated. I'm not suggesting it's simple. I want a guy who knows enough math so that he can use those tools effectively but has a curiosity about how things work and enough imagination and tenacity to dope it out.

Why are the numbers so good this year for your hedge fund, Medallion?

Once in a while the phenomena we exploit are particularly present. We like a reasonable amount of volatility. In our business we want some action.

Yet for many firms the market has proved increasingly difficult.

Many of the anomalies we initially exploited are intact, though they have weakened some. What you need to do is pile them up. You need to build a system that is layered and layered. And with each new idea, you have to determine, Is this really new, or is this somehow embedded in what we've done already? So you use statistical tests to determine that, yes, a new discovery is really a new discovery. Okay, now how does it fit in? What's the right weighting to put in? And finally you make an improvement. Then you layer in another one. And another one.

Are markets more efficient than when you started?

Considerably more efficient. There was a time when we were trading Treasury bills and we were looking at the discount structure of the bills. We said, Something is crazy here. Far-out bills were trading at some huge discount, but the 12-month physical bill was not exhibiting any such discount. Something was wrong. This was certainly something that a Long-Term Capital Management would have eliminated in a microsecond. So we just kept looking at it and saying, Why is this? The answer was that no one was picking up that inefficiency. So we bought up a whole bunch of Treasury bill futures, hedged the position in various ways, kept our fingers crossed, and sure enough, it came in. It could have gone the other way, I suppose, but not for very long, because the chickens had to come home to roost. But those kinds of opportunities don't exist now. The commodities markets used to trend pretty heavily -- long-term trends -- but those don't really exist anymore.

Long-Term Capital Management was, like Renaissance, a quantitative trading firm. Did you learn any lessons from its collapse?

Everyone in the company read the book about LTCM. It makes you wary in a general sense. Our approach is very different. We don't start with models. We start with data. We don't have any preconceived notions. We look for things that can be replicated thousands of times. A trouble with convergence trading is that you don't have a time scale. You say that eventually things will come together. Well, when is eventually?

How did LTCM's collapse affect you?

If anything, it was positive. We did very well during that period. Tumult is usually good for us. We don't have credit lines of any significance. We don't do a lot of leveraged-type financing. People were calling us from various banks asking us about our balance sheets. I had our guys calling our counterparties: "Tell me about your problems." Generally, those kinds of times -- and also in '94 -- when everyone is running around like a chicken with its head cut off, that's pretty good for us because they seem to evidence the patterns that we know how to take advantage of.

Is there a size limit for a firm like Renaissance?

There undoubtedly is, but frequently one does not discover that number until after you're past it. The budget this year is to end with 150 people. If you were to have asked me five years ago, "Could you run Renaissance with 150 people efficiently?" I would have said, "What the hell would they be doing?" That's why we're on the third expansion of this building. For years people have asked me, "How much money can you manage?" And my honest answer has been, "About twice as much as we now manage." And that's still my answer. We now manage a little less than $4 billion. Can we manage $7 billion or $8 billion? Yes. Could we manage $70 billion? Of course not. I wouldn't have a clue as to how to manage that. It's inconceivable to me to manage that much doing what we do now, but maybe new things would come along. They always have.

Are you prouder of your mathematical legacy, or of this firm?

I would say about equal. The math stuff I did, the outsize reputation that some of it received, came well after I stopped doing it. I wouldn't say that either one is a source of more satisfaction.

Did you always want to be more than an academic?

In college, while I was busy learning mathematics, it occurred to me to start a movie theater. There was only the Brattle Theater in Cambridge. And I thought maybe there's room for another one. Fortunately, I did not start a movie theater. There were periods when I would only think about mathematics, but then I would think, "Gee, maybe there's something else." Through high school, anything related to business seemed absurd to me.

Will you retire anytime soon?

To myself, I have said, "I'm 62; by the time I'm 65, I'd like to pass the baton."

When you retire, will you go back to math?

There are other things I would like to do. I have a charitable foundation. I'd like to travel. But I expect I would try anyway and go back and do some mathematics until the point it occurred to me that this was a waste of my time. I don't know how quickly that would be. But I'd try, yeah.

Strategy Trading---Benchmarking James Simons I

James Simons

誰是2005年收入最高的“打工者”？是華爾街薪酬最高的金融家？高盛前行政總裁鮑爾森(Henry M. Paulson Jr.)去年的薪金收入是3830萬美元，加上股票期權共不到1億美元。還是世界500強的行政總裁？去年《福布斯》排行榜中最賺錢的行政總裁是第一資本金融公司(Capital One Financial Corp.)的掌門人費爾班克(Richard Fairbank)，一年進帳2.5億美元。
　　
　　答案是以上皆非。根據投資雜誌《機構投資者的阿爾法》(Institutional Investor's Alpha)報導，2005年年度收入最高的是文藝復興科技公司(Renaissance Technologies Corp.)的主席西蒙斯(James H. Simons)，年收入高達15億美元，聞名全球的投機大鱷索羅斯(George Soros)則只排第三，年收入達8.4億美元。
　　
　　或許你對西蒙斯這個名字很陌生；即使是在華爾街專業從事投資基金的人，也很少聽說過西蒙斯和他的文藝復興科技公司。雖然行事低調且不為外人所知，但無論是從毛回報率還是淨回報率計算，西蒙斯都是這個地球上最偉大的對沖基金經理之一。
　　
　　現年68歲的西蒙斯曾經是大學數學教授，與華裔數學家陳省身一同創立了著名的Chern-Simons幾何定律，後於1982年創立文藝復興科技公司，1988年3月成立公司的旗艦產品——大獎章基金(Medallion Fund)，2001年曾到清華大學做過學術報告，並捐款設立了陳-西蒙斯樓(Chern-Simons Hall)專家公寓。
　　
　　經歷了1998年俄羅斯債券危機和2001年高科技股泡沫危機，許多曾經聞名遐邇的對沖基金經理都走向衰落。羅伯遜(Julian Robertson)關閉了老虎基金，梅利韋瑟的(John Meriwether)的長期資本管理公司幾乎破產，索羅斯的量子基金也大幅縮水。與之相比，西蒙斯的大獎章基金的平均年淨回報率則高達34%。從1988成立到1999年12月，大獎章基金總共獲得了2,478.6%的淨回報率，是同時期中的第一名；第二名是索羅斯的量子基金，有1,710.1%的回報；而同期的標準普爾指數僅是9.6%。不過，文藝復興科技公司所收取的費用，更高得令人咋舌。一般對沖基金的管理費及利潤分成的比率分別為2%和20%。但文藝復興所收取的費用分別為5%和44%，幾乎與客戶對分利潤，怪不得西蒙斯的年薪能高達15億美元。
　　
　　《美國海外投資基金目錄》(U.S. Offshore Funds Directory)的作者本海姆(Antoine Bernheim)指出，西蒙斯創造的回報率比布魯斯·科夫勒(Bruce Kovner)、喬治·索羅斯、保羅·都鐸·鐘斯(Paul Tudor Jones)、路易士·培根(Louis Bacon)、馬克·金頓(Mark Kingdon)等傳奇投資大師都要高出10個百分點，在對沖基金業內幾乎無出其右。作為一個交易者，西蒙斯正在超越有效市場假說；有效市場假說認為市場價格波動是隨機的，交易者不可能持續從市場中獲利。
　　
　　西蒙斯生於波士頓郊區牛頓鎮，是一個制鞋廠老闆的兒子，3歲就立志成為數學家。從牛頓高中畢業後，他進入麻省理工學院，從師於著名的數學家安布羅斯(Warren.Ambrose)和辛格(I.M.Singer)。1958年，他獲得了學士學位，僅僅三年後，他就拿到了加州大學伯克利分校的博士學位，一年後他成為哈佛大學的數學系教授。西蒙斯很早就與投資結下緣份，在1961年，他和麻省理工學院的同學投資于哥倫比亞地磚和管線公司；在伯克利，他嘗試做股票交易，但是交易結果並不太好。
　　
　　1964年，他離開了大學校園，進入美國國防部下屬的一個非盈利組織——國防邏輯分析協會，並進行代碼破解工作。沒過多久，《時代週刊》上關於越南戰爭的殘酷報導讓他意識到他的工作實際上正在幫助美軍在越南的軍事行動，反戰的他於是向《新聞週刊》寫信說應該結束戰爭。當他把他的反戰想法告訴老闆，很自然的被解雇了。
　　
　　他又回到了學術界，成為紐約州立石溪大學(Stony Brook University)的數學系主任，在那裏做了8年的純數學研究。1974年，他與陳省身聯合發表了著名的論文《典型群和幾何不變式》，創立了著名的Chern-Simons理論，該幾何理論對理論物理學具有重要意義，廣泛應用於從超引力到黑洞。1976年，西蒙斯獲得了每5年一次的全美數學科學維布倫(Veblen)獎金，這是數學世界裏的最高榮耀。
　　
　　在理論研究之餘，他開始醉心於股票和期貨交易。1978年，他離開石溪大學創立私人投資基金Limroy，該基金投資領域廣泛，涉及從風險投資到外匯交易；最初主要採用基本面分析方法，例如通過分析美聯儲貨幣政策和利率走向來判斷市場價格走勢。
　　
　　十年後，西蒙斯決定成立一個純粹交易的對沖基金。他關閉了Limroy，並在1988年3月成立了大獎章基金，最初主要涉及期貨交易。1988年該基金盈利8.8%，1989年則開始虧損，西蒙斯不得不在1989年6月份停止交易。在接下來的6個月中，西蒙斯和普林斯頓大學的數學家勒費爾(Henry Larufer)重新開發了交易策略，並從基本面分析轉向數量分析。
　　
　　大獎章基金主要通過研究市場歷史資料來發現統計相關性，以預測期貨、貨幣、股票市場的短期運動，並通過數千次快速的日內短線交易來捕捉稍縱即逝的市場機會，交易量之大甚至有時能占到整個NASDAQ交易量的10%。當交易開始，交易模型決定買賣品種和時機，20名交易員則遵守指令在短時間內大量的交易各種美國和海外的期貨，包括商品期貨、金融期貨、股票和債券。但在某些特定情況下，比如市場處在極端波動的時候，交易會切換到手工狀態。
　　
　　經過幾年眩目的增長，大獎章基金在1993年達到2.7億美元，並開始停止接受新資金。1994年，文藝復興科技公司從12個雇員增加到36個，並交易40種的金融產品。現在，公司有150個雇員，交易60種金融產品，基金規模則有50億美元。在150名雇員中有三分之一是擁有自然科學博士學位的頂尖科學家，涵蓋數學、理論物理學、量子物理學和統計學等領域。所有雇員中只有兩位是華爾街老手，而且該公司既不從商學院中雇用職員，也不從華爾街雇用職員，這在美國投資公司中幾乎是獨一無二的。（摘自卓越理財 趙仲峰）

如果管理的資產過於龐大，對沖基金要想實現高於業內平均水準的回報率就越來越難了，對吧？
　　把這點告訴詹姆斯.西蒙斯(James Simons)。
　　西蒙斯既是世界級的數學大師，又是Renaissance Technologies Corp.的老闆，最偉大的對沖基金經理之一。眼下，他準備設立一隻規模可能高達1,000億美元的基金的消息在業內鬧得沸沸揚揚，要知道，這可是整個對沖基金行業資產管理總額的十分之一左右。從早期的推廣資料來看，這只基金的最低投資額為2,000萬美元，面向機構投資者發售。
　　據估計，西蒙斯目前的資產淨值約為25億美元。Renaissance旗下的核心業務──規模為50億美元的大獎章（Medallion）對沖基金自1988年成立以來，年均回報率高達34%，堪稱在此期間表現最佳的對沖基金。這個回報率已經扣除了5%的資產管理費以及44%的投資收益分成等費用因素，並且經過審計。大獎章基金收取的這兩項費用是對沖基金平均收費水準的兩倍以上。
　　2005年，西蒙斯成為全球收入最高的對沖基金經理，淨賺15億美元，差不多是索羅斯的兩倍；從1988年開始，他所掌管的大獎章基金年均回報率高達34％，15年來資產從未減少過。西蒙斯幾乎從不雇用華爾街的分析師，他的文藝復興科技公司裏坐滿了數學和自然科學的博士。用數學模型捕捉市場機會，由電腦作出交易決策，是這位元超級投資者成功的秘訣。
　　“人們一直都在問我，你賺錢的秘密是什麼？”幾乎每次接受記者採訪時，詹姆斯.西蒙斯(James Simons)總會說到這句話，他似乎已經習慣了那些渴望的眼神。事實上，在對沖基金的世界裏，那應該是每個人都想要瞭解的秘密。Medallion不願透露運作策略的細節，甚至對自己的投資者也是這樣。也有其他的基金採取了相同的策略，卻遠沒有大獎章基金這樣成功。
　　西蒙斯曾經和華裔科學家陳省身共同創立了著名的Chern-Simons定律，也曾經獲得過全美數學界的最高榮譽。
　　大獎章基金已經有12年沒有吸收新的資金了。現年67歲的西蒙斯一直在向現有投資者提供回報。他也相信，基金如果規模太大收益率就會下滑。實際上，Renaissance據估計會在年底時向外部投資者返還資金餘額，這樣西蒙斯和他的雇員就會成為大獎章基金唯一的投資者。到時候，基金的規模與現在將大體相仿。與少數投資者打交道有助於不善拋頭露面的西蒙斯避開媒體的追蹤。
　　西蒙斯的最新舉動看起來與Renaissance不願讓資產超過一定範圍的做法格格不入。確實，許多基金經理都發現資產增加將束縛業績的增長。對新基金有大致瞭解的投資者表示，這只基金將不同於大獎章基金現有的對沖基金，新基金希望通過設定較為溫和的目標回報率來吸收更多資金。
　　這只新基金是表明對沖基金爭取退休金計畫等機構投資者客戶、搶佔共同基金等傳統理財公司地盤的最新跡象。這只基金將使用六十幾位元數學家和物理學博士共同開發的模型。這只基金把自己的回報率定為強於標準普爾500指數，並力爭實現較為穩定的業績。
　　投資者表示，儘管西蒙斯在華爾街並非盡人皆知，但他以往的成就讓投資者對這只基金產生了濃厚的興趣。《美國海外基金目錄》(U.S. Offshore Funds Directory)的作者本海姆(Antoine Bernheim)說，Renaissance自1998年以來34%的年均回報率在對沖基金業內傲視群雄。就算索羅斯(George Soros)的量子基金(Quantum Fund)，同期年均回報率也只有22%，而標準普爾500指數同期的年均漲幅才只有9.6%。本海姆指出，西蒙斯創造的回報率比布魯斯.科夫勒(Bruce Kovner)、索羅斯、保羅.特德.鐘斯(Paul Tudor Jones)、路易士.培根(Louis Bacon)、馬克.金登(Mark Kingdon)等傳奇投資大師高出10個百分點，在對沖基金業內他堪稱出類拔萃。
　　過去兩年來，大獎章基金的月資產從未減少過。一位投資者透露，過去幾年來大獎章基金向投資者提供了豐厚的回報，但投資者卻不能把這些巨額回報用於追加對大獎章基金的投資。不過，這也有可能會刺激投資者對新基金的興趣。
　　大獎章基金在宣傳資料上是這樣寫的：雖然以往的優異表現不能保證新基金也一定獲得成功，但新基金也會採用大獎章基金科學的運作策略，並以大獎章基金的技術為基石。新基金將採取截然不同的運作策略：偏重於投資美國股市，持有頭寸超過一年。西蒙斯將通過下調收費（如把資產管理費的費率定在2%左右）來吸引投資。但與此同時，他可能要更多披露資金運作上的細節。這是因為退休金計畫及他們的顧問往往要求受雇的理財公司全面披露投資策略的簡要情況。
　　以下是一些和西蒙斯有關的數字：1988年以來，西蒙斯掌管的的大獎章（Medallion）對沖基金年均回報率高達34%，這個數字較索羅斯等投資大師同期的年均回報率要高出10個百分點，較同期標準普爾500指數的年均回報率則高出20多個百分點；從2002年底至2005年底，規模為50億美元的大獎章基金已經為投資者支付了60多億美元的回報。
　　這個回報率是在扣除了5％的資產管理費和44％的投資收益分成以後得出的，並且已經經過了審計。值得一提的是，西蒙斯收取的這兩項費用應該是對沖基金界最高的，相當於平均收費標準的兩倍以上。高額回報和高額收費使西蒙斯很快成為超級富豪，在《福布斯》雜誌2006年9月發佈的“400位最富有的美國人”排行榜中，西蒙斯以40億美元的身家躋身第64位。
　　
　　模型先生
　　針對不同市場設計數量化的投資管理模型，並以電腦運算為主導，在全球各種市場上進行短線交易是西蒙斯的成功秘訣。不過西蒙斯對交易細節一直守口如瓶，除了公司的200多名員工之外，沒有人能夠得到他們操作的任何線索。
　　對於數量分析型對沖基金而言，交易行為更多是基於電腦對價格走勢的分析，而非人的主觀判斷。文藝復興公司主要由3個部分組成，即電腦和系統專家，研究人員以及交易人員。西蒙斯親自設計了最初的數學模型，他同時雇用了超過70位擁有數學、物理學或統計學博士頭銜的人。西蒙斯每週都要和研究團隊見一次面，和他們共同探討交易細節以及如何使交易策略更加完善。
　　作為一位數學家，西蒙斯知道靠幸運成功只有二分之一的概率，要戰勝市場必須以周密而準確的計算為基礎。大獎章基金的數學模型主要通過對歷史資料的統計，找出金融產品價格、宏觀經濟、市場指標、技術指標等各種指標間變化的數學關係，發現市場目前存在的微小獲利機會，並通過杠杆比率進行快速而大規模的交易獲利。
　　文藝復興科技公司的旗艦產品——大獎章基金成立於1988年3月，到1993年，基金規模達到2.7億美元時開始停止接受新資金。現在大獎章基金的投資組合包含了全球上千種股市以及其他市場的投資標的，模型對國債、期貨、貨幣、股票等主要投資標的的價格進行不間斷的監控，並作出買入或賣出的指令。
　　當指令下達後，20名交易員會通過數千次快速的日內短線交易來捕捉稍縱即逝的機會，交易量之大甚至有時能占到整個納斯達克市場交易量的10%。不過，當市場處於極端波動等特殊時刻，交易會切換到手工狀態。
　　和流行的“買入並長期持有”的投資理念截然相反，西蒙斯認為市場的異常狀態通常都是微小而且短暫的，“我們隨時都在買入賣出賣出和買入，我們依靠活躍賺錢”，西蒙斯說。
　　西蒙斯透露，公司對交易品種的選擇有三個標準：即公開交易品種、流動性高，同時符合模型設置的某些要求。他表示，“我是模型先生，不想進行基本面分析，模型的優勢之一是可以降低風險。而依靠個人判斷選股，你可能一夜暴富，也可能在第二天又輸得精光。”　　西蒙斯的所作所為似乎正在超越有效市場假說：有效市場假說認為市場價格波動是隨機的，交易者不可能持續從市場中獲利。而西蒙斯則強調，“有些交易模式並非隨機，而是有跡可循、具有預測效果的。”如同巴菲特曾經指出“市場在多數情況下是有效的，但不是絕對的”一樣，西蒙斯也認為，雖然整體而言，市場是有效的，但仍存在短暫的或局部的市場無效性，可以提供交易機會。
　　在接受《紐約時報》採訪時，西蒙斯提到了他曾經觀察過的一個核子加速器試驗，“當兩個高速運行的原子劇烈碰撞後，會迸射出數量巨大的粒子。”他說，“科學家的工作就是分析碰撞所帶來的變化。”　　“我注視著電腦螢幕上粒子碰撞後形成的軌跡圖，它們看似雜亂無章，實際上卻存在著內在的規律，”西蒙斯說，“這讓我自然而然地聯想到了證券市場，那些很小的交易，哪怕是只有100股的交易，都會對這個龐大的市場產生影響，而每天都會有成千上萬這樣的交易發生。”西蒙斯認為，自己所做的，就是分析當交易這只蝴蝶的翅膀輕顫之後，市場會作出怎樣複雜的反應。
　　“這個課題對於世界而言也許並不重要，不過研究市場運轉的動力非常有趣。這是一個非常嚴肅的問題。”西蒙斯笑起來的時候簡直就像一個頑童，而他的故事，聽起來更像是一位精通數學的書生，通過複雜的賠率和概率計算，最終打敗了賭場的神話。這位前美國國防部代碼破譯員和數學家似乎相信，對於如何走在曲線前面，應該存在一個簡單的公式，而發現這個公式則無異於拿到了通往財富之門的入場券。
　　黑箱作業　
　　Renaissance的投資者、Protege Partners LLC的總裁兼首席投資長傑弗瑞.塔倫特(Jeffrey Tarrant)表示，Renaissance現在基本上是黑箱作業，它的工作人員發誓要保守秘密，採取的是自營交易的運作策略。對沖基金行業一直擁有“黑箱作業”式的投資模式，可以不必向投資者披露其交易細節。而在一流的對沖基金投資人之中，西蒙斯先生的那只箱子據說是“最黑的”。
　　就連優秀的數量型對沖基金經理也無法弄清西蒙斯的模型究竟動用了哪些指標，“我們信任他，相信他能夠在股市的驚濤駭浪中遊刃有餘，因此也就不再去想電腦都會幹些什麼之類的問題”，一位大獎章基金的長期投資者說。當這位投資者開始描述西蒙斯的投資方法時，他坦承，自己完全是猜測的。
　　不過，每當有人暗示西蒙斯的基金缺乏透明度時，他總是會無可奈何地聳聳肩，“其實所有人都有一個黑箱，我們把他稱為大腦。”西蒙斯指出，公司的投資方法其實並不神秘，很多時候都是可以通過特定的方式來解決的。當然，他不得不補充說，“對我們來說，這其實不太神秘。”　　在紐約，有一句名言是：你必須非主流才能入流（You have to be out to be in），西蒙斯的經歷似乎剛好是這句話的注解。在華爾街，他的所做所為總是讓人感到好奇。
　　西蒙斯在越戰期間違反了軍紀，之後就投身於理財行業。西蒙斯的文藝復興科技公司總部位於紐約長島，那座木頭和玻璃結構的一層建築從外表看上去更像是一個普通的腦庫，或者是數學研究所。和很多基金公司不同的是，文藝復興公司的心臟地帶並不是夜以繼日不停交易的交易室，而是一間有100個座位的禮堂。每隔半個月，公司員工都會在那裏聽一場科學演講。“有趣而且實用的統計學演講，對你的思想一定會有所啟發。”一位元元喜歡這種學習方式的員工說。
　　令人驚訝的還不止這些。西蒙斯一點也不喜歡華爾街的投資家們，事實上，如果你想去文藝復興科技公司工作的話，華爾街經驗反而是個瑕疵。在公司的200多名員工中，將近二分之一都是數學、物理學、統計學等領域頂尖的科學家，所有雇員中只有兩位是金融學博士，而且公司從不雇用商學院畢業生，也不雇用華爾街人士，這在美國的投資公司中堪稱絕無僅有。
　　“我們不雇用數理邏輯不好的學生”，曾經在哈佛大學任教的西蒙斯說。“好的數學家需要直覺，對很多事情的發展總是有很強的好奇心，這對於戰勝市場非常重要。”文藝復興科技公司擁有一流的科學家，其中包括貝爾試驗室的著名科學家Peter Weinberger和佛吉尼亞大學教授Robert Lourie。他還從IBM公司招募了部分熟悉語音識別系統的員工。“交易員和語音識別的工作人員有相似之處，他們總是在猜測下一刻會發生什麼。”　　人員流動幾乎是不存在的。每6個月，公司員工會根據業績收到相應的現金紅利。據說半年內的業績基準是12%，很多時候這個指標可以輕鬆達到，不少員工還擁有公司的股權。西蒙斯很重視公司的氣氛，據說他經常會和員工及其家屬們分享週末，早在2000年，他們就曾一起飛去百慕大度假。與此同時，每一位員工都發誓要保守公司秘密。
　　近年來，西蒙斯接受最多的質疑都與美國長期資本管理公司(LTCM)有關。LTCM在上世紀90年代中期曾經輝煌一時，公司擁有兩位諾貝爾經濟學獎得主，他們利用電腦處理大量歷史資料，通過精密計算得到兩個不同金融工具間的正常歷史價格差，然後結合市場訊息分析它們之間的最新價格差。如果兩者出現偏差，電腦立即發出指令大舉入市；經過市場一段時間調節，放大的偏差會自動恢復到正常軌跡上，此時電腦指令平倉離場，獲取偏差的差值。
　　LTCM始終遵循“市場中性”原則，即不從事任何單方面交易，僅以尋找市場或商品間效率落差而形成的套利空間為主，通過對沖機制規避風險，使市場風險最小。但由於其模型假設前提和計算結果都是在歷史統計資料基礎上得出的，一旦出現與計算結果相反的走勢，則對沖就變成了一種高風險的交易策略。
　　而在極大的杠杆借貸下，這種風險被進一步放大。最輝煌時，LTCM利用從投資者籌得的22億美元資本作抵押，買入價值1250億美元證券，然後再以證券作為抵押，進行總值12500億美元的其他金融交易，杠杆比率高達568倍。短短4年中，LTCM曾經獲得了285%的收益率，然而，在過度操縱之下，又在僅兩個月之內又輸掉了45億美元，走向了萬劫不復之地。
　　“我們的方式和LTCM完全不同”，西蒙斯強調，文藝復興科技公司沒有、也不需要那麼高的杠杆比例，公司在操作時從來沒有任何先入為主的概念，而是只尋找那些可以複製的微小的獲利瞬間，“我們絕不以‘市場恢復正常’作為賭注投入資金，有一天市場終於會正常的，但誰知道是哪一天。”　　西蒙斯的擁護者們也多半對黑箱操作的風險不以為然，他們說，“長期資本公司只有兩位諾貝爾獎金獲得者充當門面，主要的還是華爾街人士，他們的賭性決定了終究會出錯”，另一位著名的數量型基金管理人也表示，“難以相信在西蒙斯的方法中會沒有一些安全措施。”他指出，西蒙斯的方法和LTCM最重要的區別是不涉及對沖，而多是進行短線方向性預測，依靠同時交易很多品種、在短期作出大量的交易來獲利。具體到每一個交易的虧損，由於會在很短的時間內平倉，因此損失不會很大；而數千次交易之後，只要盈利交易多餘虧損交易，總體交易結果就是盈利的。
　　數學大師
　　西蒙斯很少在金融論壇上發表演講，他喜歡的是數學會議，他在一個幾何學研討會上慶祝自己的60歲生日，為數學界和患有孤獨症的兒童捐錢，在發表演講時，更常常強調是數學使他走上了投資的成功之路。有人說，和華爾街的時尚毫不沾邊或許也是他並不矚目的原因之一。
　　西蒙斯在數學方面有著天生的敏感和直覺，這個制鞋廠老闆的兒子3歲就立志成為數學家。高中畢業後，他順利地進入了麻省理工學院，大學畢業僅三年，就拿到了加州大學伯克利分校的博士學位，24歲就出任哈佛大學數學系教授。
　　不過，儘管已經是國際數學界的後起之秀，他還是很快就厭倦了學術生涯。1964年，天生喜歡冒險的西蒙斯進入美國國防部下屬的一個非盈利組織——國防邏輯分析協會進行代碼破解工作。後來由於反對越戰，他又重回學術界，成為紐約州立石溪大學(Stony Brook University)的數學系主任，在那裏做了8年的純數學研究。
　　西蒙斯很早以前就曾和投資結緣，1961年，他和麻省理工學院的同學投資于哥倫比亞地磚和管線公司；在伯克利時也曾投資一家婚禮禮品的公司，但結果都不太理想，當時他覺得股市令人煩惱的，“我還曾經找到美林公司的經紀人，試圖做些大豆交易”，西蒙斯說。
　　直到上世紀70年代早期，西蒙斯才開始真正對投資著迷。那時他還在石溪大學任教，他身邊的一位數學家參與了一家瓷磚公司出售的交易，“8個月的時間裏賺了我10倍的錢。”　　70年代末，當他離開石溪大學創立私人投資基金時，最初也採用基本面分析的方式，“我沒有想到用科學的方法進行投資，”西蒙斯說，那一段時間他主要投資於外匯市場，“隨著經驗的不斷增加我想到也許可以用一些方法來製作模型，預見貨幣市場的走勢變動。”　　80年代後期，西蒙斯和普林斯頓大學的數學家勒費爾(Henry Larufer)重新開發了交易策略，並從基本面分析轉向數量分析。從此，西蒙斯徹底轉型為“模型先生”，並為大獎章基金接近500位投資人創造出了令人驚歎的業績。
　　2005年，西蒙斯宣佈要成立一隻規模可能高達1000億美元的新基金，在華爾街轟動一時，要知道，這個數字幾乎相當於全球對沖基金管理資產總額的十分之一。談到新基金時，西蒙斯更加謹慎，他表示，和大獎章基金主要針對富有階層不同，新基金的最低投資額為2000萬美元，主要面向機構投資者，將通過下調收費來吸引投資；此外，新基金將偏重於投資美國股市，持有頭寸超過一年——相對於大獎章的快速交易而言，新基金似乎開始堅持“買入並持有”的理念。
　　“對大獎章非常有效的模型和方法並不一定適用於新基金”，看來西蒙斯相信，對於一個金額高達千億的對沖基金來說，如果還採用類似於大獎章的操作方法的話，一定是非常冒險的。
　　儘管新基金有著良好的血統，不過不少投資者仍然懷疑它究竟能有多大的作為，一個起碼的事實是，相對於一些流動性差的小型市場而言，高達1000億美元的基金規模可能顯得太大，這將增加它們在退出時的困難。
　　儘管懷疑的聲音很多，到2006年2月中旬，詹姆斯.西蒙斯還是籌集到了40億美金，並表示將吸收更多的資金。公司同時向投資者承諾，一旦在任何時點基金運作出現疲弱的跡象，就將停止吸收新資金，屆時新基金將不再繼續增加到千億美金的上限。
　　截至2006年8月，這只名為文藝復興法人股票基金（Renaissance Institutional Equities Fund）的新基金，在同期標普500指數漲幅為4%的情況下錄得了13％的增長。

由機構投資者Alpha雜誌進行的調查24日顯示，全球排名前25位的對沖基金經理的總收入接近150億美元，這個數值超過了約旦去年的國民收入，其中文藝復興科技公司的詹姆斯·西蒙斯以17億美元的年收入連續兩年高居榜首，而華爾街收入最多的總裁—高盛集團首席執行官布蘭克芬的年收入也僅僅是5430萬美元。

    今年的排行榜中，首次出現了三位基金經理的收入均超過10億美元的情形。Citadel投資集團的肯尼斯·格裏芬以年收入14億美元排名第二，ESL投資的埃弗·蘭派特收入則達13億美元。該排行榜是對沖基金行業的標準，也是那些希望在該行業逐利的人所必讀的東西。

    據《紐約時報》報導，24日公佈的排行榜結果表明，對沖基金經理當中的富人階層正在以極快的速度積累著更多的財富。

    據悉，要想“榮登”Alpha雜誌今年的這一排行榜，對沖基金經理2006年的年收入必須超過2.4億美元，幾乎是去年排行榜要求的基金經理2005年年收入的兩倍。而在2001年和2002年，這個收入水準的“門檻”僅在3000萬美元。

    今年69歲、曾經當過數學教授的詹姆斯·西蒙斯照例出現在榜首，他2005年的年收入達15億美元。西蒙斯管理著最受尊敬的對沖基金之一Medallion。該基金運用數學策略，是定量型基金的一種，自1988年成立以來，年均回報率高達34％。這個回報率已經扣除了5％的資產管理費以及44％的投資收益分成等費用因素，並且經過審計。（摘自深圳商報 2007.4.25  陳相明）