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广州集思未来金融经济学专题-数据科学与时间序列模型的应用

广州集思未来金融经济学专题-数据科学与时间序列模型的应用

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课程介绍

金融经济学专题:数据科学与时间序列模型的应用以金融股票预测与经济数据运动行为研究为例


课时安排: 7周在线小组科研学习+5周不限 时论文指导学习

适合人群 | Prerequisites

适合年级(Grade): 高中生/大学生
适合专业(Major): 应用数学、金融经济学、宏观经济学、计量经济学、金融数据分析、股票投资、商业分析等专业或希望修读相关专业的学生;学生需具备随机变量、概率论等相关知识并熟练掌握R语言。

导师介绍 | Instructor Introduction

  Peter——麻省理工学院(MIT),终身教职
  Peter导师以优异成绩获得哈佛大学(Harvard University)应用数学学士学位,并当选为Phi Beta Kappa Alpha Chapter的成员。后续他攻读统计学硕士学位,并获得了伦敦大学帝国理工学院(University of London)的硕士学位和文凭,以及加州大学伯克利分校(University of California Berkeley)的博士学位。在哈佛大学担任统计学教授期间,他获得了美国科学基金会的博士后数学研究奖学金。随后,他成为麻省理工学院斯隆管理学院(MIT Sloan School of Management)的教授,并晋升为管理科学终身教授。从1990年到1998年,他还担任麻省理工学院斯隆管理学院(MIT Sloan School of Management)的首席研究科学家,在经济和管理科学计算研究中心(CCREMS)和国际金融服务研究中心(IFSRC)进行研究。他是风险管理项目组的积极成员,并开发了纳入行业标准RiskMetrics方法论的分析方法。2013年,他加入MIT数学系,担任金融数学和统计讲师。2014年在北京交通大学暑期学校任教期间,被聘为计算机与信息技术学院特聘教授。
  自1992年以来,他一直通过他的公司Kempthorne analytics,Inc.为各种机构提供金融和统计分析咨询服务。过去的客户包括花旗银行(Citibank)、Colonial/Liberty Funds、美国运通(American Express)、巴黎银行(Banque Nationale de Paris)、佳能(Canon)、富达管理与研究(Fidelity Management and Research)、Mathsoft/Corporation、默克(Merck)、RXR、山德士(Sandoz)和普林斯顿品牌计量经济学(Princeton Brand Econometrics)。项目活动包括:股票市场的资产选择建模、风险管理的统计分析、风险管理软件的集成设计和实现、衍生品定价的金融分析、灾难性风险分析——风险暴露建模和保险定价方案、用于做市的股票市场交易数据微观结构建模以及交易系统的设计、开发、实现。
  自1995年以来,他一直担任投资经理,利用先进的统计分析来管理各种投资项目。从2010年到2012年,他在IKOS,CIF Ltd担任投资组合经理和研究员,IKOS,CIF Ltd是一家完全系统化的量化对冲基金,管理着21亿美元(美元)的股票、期货和货币投资组合。作为投资组合经理,管理和增强股票投资组合的实时构建过程,包括alpha模型评估和开发、执行分析和投资组合优化;他还进行了期货和货币投资组合的风险建模和管理。作为研究员,他担任研究指导委员会,管理和指导研究人员,并协调IKOS/牛津大学博士实习生计划。他于1995年联合创立了Chronos Asset Management,并于1996年联合创立了Summa Capital Management。作为这两家投资管理公司的负责人,他运用自己专有的分析方法开发统计交易模型和交易系统,并监督交易操作。
  Kempthorne Analytics目前在马萨诸塞州注册为投资顾问,为零售客户管理系统定量投资项目。Peter导师持有Series 3和Series 65许可证,并在the National Futures Association是注册商品交易顾问。
  他活跃于John Bertram House Inc.(1998-2010)和Lynn Home for Young Women,Inc.(2005-2010)的董事会。他曾担任两家非营利公司的财务主管,并担任监督信托资产管理的财务委员会。
  Peter received his A.B.magna cum laude degree in applied mathematics from Harvard University and was elected to the Alpha Chapter of Phi Beta Kappa.He pursued graduate studies in statistics receiving the M.Sc.degree and the Diploma of Imperial College award from the University of London,and a Ph.D.from the University of California Berkeley.While an Assistant Professor of Statistics at Harvard,he was awarded a Postdoctoral Mathematical Sciences Research Fellowship by the National Science Foundation.He then joined the faculty of MIT at the Sloan School of Management as a visiting Assistant Professor and was promoted to Associate Professor of Management Science.From 1990 to 1998,he also served as a Principal Research Scientist at the MIT Sloan School of Management conducting research at the Center for Computational Research in Economics and Management Science(CCREMS)and at the International Financial Services Research Center(IFSRC).He was an active member of the Risk Management Working Group and developed analytics incorporated in the industry-standard RiskMetrics methodology.In 2013 Peter joined the MIT mathematics department as a lecturer in financial mathematics and statistics.His course"Topics in Mathematics with Applications to Finance"is published and available on the MIT Open Courseware website.In 2014,while teaching in the Global Summer School of Beijing Jiaotong University,he was appointed Distinguished Visiting Professor in the School of Computer and Information Technology.
  Peter has been providing consulting services in financial and statistical analytics to a wide range of institutions through his company since 1992.Past clients include Citibank,Colonial/Liberty Funds,American Express,Banque Nationale de Paris,Canon,Fidelity Management and Research,Mathsoft/Insightful Corporation,Merck,RXR,Sandoz,and Princeton Brand Econometrics.Project activities include:asset selection modeling for equity markets,statistical analytics for risk management,integrated design and implementation of risk management software,financial analytics for derivatives pricing,catastrophic risk analytics-exposure modeling and pricing insurance programs,stock market microstructure modeling of transaction data for market making;and trading system design,development,and implementation.
  Since 1995,Peter has been an investment manager,exploiting advanced statistical analytics to manage a variety of investment programs.From 2010-2012 he was portfolio manager and senior researcher at IKOS,CIF Ltd,a fully systematic,quantitative hedge fund managing$2.1B(USD)in global portfolios of equities,futures,and currencies.As portfolio manager he managed and enhanced real-time construction processes of equities portfolios,including alpha model evaluation and development,executions analysis and portfolio optimization;and he conducted risk modeling and management of futures and currency portfolios.As senior researcher he chaired the Research Steering Committee,managed and mentored researchers,and coordinated the IKOS/Oxford Univ.PhD intern program.He co-founded Chronos Asset Management in 1995 and Summa Capital Management in 1996.As a principal of both investment management companies,he applied his proprietary analytic methods to develop statistical trading models and trading systems and supervised trading operations.
  Kempthorne Analytics is currently registered as an investment adviser in Massachusetts and manages systematic quantitative investment programs for retail clients.Peter holds the Series 3 and Series 65 licenses and is registered with the National Futures Association as a Commodity Trading Adviser.
  Peter was active on the boards of John Bertram House Inc.(1998-2010)and the Lynn Home for Young Women,Inc.(2005-2010).He served as Treasurer for both non-profit corporations and chaired the finance committees that oversaw the management of trust assets.
  任职学校
  麻省理工学院(MIT)创立于1861年,是世界私立研究型大学,在2020年U.S.News世界大学排名中综排位列第二。学校孕育了90位诺贝尔奖得主、59位美国科学奖章获得者,以及75位麦克阿瑟奖获得者。

项目背景 | Program Background

  时间序列是指将某种现象某一个统计指标在不同时间上的各个数值,按时间先后顺序排列而形成的序列。时间序列法是一种定量预测方法,亦称简单外延方法,在统计学中作为一种常用的预测手段被广泛应用。时间序列分析在第二次世界大战前应用于经济预测。二次大战中和战后,在军事科学、空间科学、气象预报和工业自动化等部门的应用更加广泛。时间序列分析(Time series analysis)是一种动态数据处理的统计方法。该方法基于随机过程理论和数理统计学方法,研究随机数据序列所遵从的统计规律,以用于解决实际问题。时间序列构成要素是:现象所属的时间,反映现象发展水平的指标数值。

项目介绍 | Program Description

  本课程将重点介绍时间序列分析的基本方法和模型及其在经济、金融数据分析中的应用。本课程将融合计算机编程的R语言辅助时间序列模型在金融经济数据中的处理分析。目前,主流经济数据分析往往会以图形方法来进行呈现,这些可视化方法被用于大数据探索、分析模型的有效性验证和数据预测结果的展现。在本课程中,导师开发并应用了趋势和季节性的重要时间序列模型,包括经典分解和多级指数平滑模型。同时导师将利用真实世界的时间序列数据(包括美国联邦储备局、世界银行和雅虎金融数据库)对本课程中涵盖的统计概率方法进行分析和实践应用。
  Introduction to fundamental methods and models of time series analysis with applications in economics,finance,and public health.The course uses R to forecast time series.Graphical methods are emphasized for data exploration,analyzing the validity of models,and presenting forecast results.Important models of trend and seasonality are developed and applied,including classical decompositions and multi-stage exponential smoothing.Real-world time series data are collected from the internet and analyzed with the methods covered in the course.

项目大纲 | Syllabus


  ●○ 时间序列分析导论  Introduction to Time Series Analysis
  ●○ 时间序列模型;金融时间序列  Simple Time Series Models;financial time series
  ●○ 预估噪声序列的时间序列相关性检验固定的流程  Testing estimated noise sequences for time series dependence;stationary processes
  ●○ 回归(AR)、移动平均(MA)和ARMA模型;模型选择和预测  Auto-regression(AR),moving average(MA),and ARMA models;model selection and forecasting
  ●○ 学术研讨1   Final Project Phase I
  ●○ 学术研讨1   Final Project Phase II
  ●○ 项目回顾和成果展示Program Review and Presentation
  ●○ 论文辅导Project Deliverables Tutoring

项目收获Program Outcome

017周在线小组科研学习+5周不限 时论文指导学习 共125课时
02项目报告
03优 秀学员获主导师Reference Letter
04EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表指导(可用于申请)
05结业证书
06成绩单

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