Nyu stern trading strategies and systems

Informática e Ciência de Dados.

Concentre-se em Informática e Ciência de Dados.

Requisitos do Programa de Ciência da Computação e Dados (12 unidades)

Cursos adicionais aprovados para contar como Computer & amp; Ciências da informação eletivas:

Notas: Os alunos também podem importar um (e apenas um) eletivo de ciência da computação com a permissão do consultor da faculdade de graduação.

- CSCI-UA 102 (Estruturas de Dados) não se sobrepõe em conteúdo com outros cursos de Stern e pode ser importado.

- CSCI-UA 4 (Introdução ao Web Design e Princípios de Computador) não conta para a Computação & amp; Concentração de dados científicos.

Bernard S. Donefer.

Professor adjunto adjunto.

44 West 4th Street, Suite 8-171.

Nova York, Nova Iorque 10012-1126.

Informação biográfica.

A carreira de Bernard Donefer em serviços financeiros incluiu o trabalho com bancos, empresas de valores mobiliários e bolsas nos EUA, Europa e Ásia, onde ocupou cargos de alto nível, incluindo a presidência de duas empresas internacionais de software financeiro. Ele é professor adjunto da Escola de Negócios da NYU Stern Graduate. Na Stern desde 2005, o Prof. Donefer ensina Sistemas de Informação Financeira e Gestão de Riscos em TI e co-ensina FinTech Risk no programa de MBA. Ele se aposentou como Distinguished Lecturer e Diretor Associado do Wasserman Trading Floor, Subotnick Financial Services Center na Zicklin Business School do Baruch College, Universidade da Cidade de Nova York. Ele possui MBA em Finanças pela Stern.

Anteriormente, vice-presidente e chefe de sistemas de mercados de capitais da Fidelity Investments em Boston, implementou um dos primeiros ambientes de negociação de ações sem papel da indústria. Ele também foi responsável por sistemas de negociação algorítmica, de renda fixa, de câmbio, mercado e mid-office, conectividade de clientes e mercado, sistema de crédito baseado em VaR em tempo real e sistema de gerenciamento de risco de mercado.

As posições anteriores incluíram a EVP / CIO do Dai Ichi Kangyo Bank (agora Mizuho), onde foi responsável por sua recuperação operacional após o bombardeio do World Trade Center de 1993 e as presidências dos sistemas de informação de serviços financeiros da Bankers Trust e CAP Information Systems, empresas internacionais de software e serviços .

O Prof. Donefer é diretor da Conatum Consulting LLC, ensinando em seminários públicos e clientes corporativos como SEC, FRB, DTCC, OCRVM, OCC, Alliance Bernstein, Getco, ITG, Harvard Management Co. nos EUA, Canadá e Europa .

Como testemunha perita em práticas de serviços financeiros, patentes de software e casos de fraude, ele atestou em tribunal federal, audiências da SEC e arbitragem de FINRA.

Ele se ofereceu com patrocinadores para oportunidades educacionais (SEO) em desenvolvimento e treinamento. Ele atuou como um conselheiro voluntário ou membro do conselho do Conjunto de Câmaras de St. Luke, dos Artistas da Humanidade de Boston, dos Poetas nas Escolas, dos Voluntários para as Artes e do Grupo de Consultoria de Voluntariado Urbano.

Um comentador freqüente da indústria, o Prof. Donefer presidiu e moderou painéis nas conferências fintech, algo, HFT e hedge funds sobre os desafios, riscos e oportunidades da negociação eletrônica nos mercados globais. Ele falou no Conselho de Relações Exteriores em Washington DC, na NY Bar Association, SIAA / FISD, TradeTech e é freqüentemente citado na imprensa nacional e internacional.

Nomeações acadêmicas.

Aposentado, Distinguido professor e Diretor Associado, Subotnick Financial Services Center, Wasserman Trading Floor, Baruch College, Zicklin Graduate School of Business, City University of New York. Anteriormente, Professor Assistente Adjunto, Fordham Graduate School of Business.

Interesses de pesquisa.

Gerenciamento de riscos Cibersegurança e privacidade Bloqueira e criptografia Microestrutura do mercado financeiro Sistemas de negociação - alta freqüência, algorítmica, gerenciamento de pedidos e roteamento.

Ensaios publicados.

Algos Gone Wild, Journal of Trading, Primavera de 2010 Não há coisa como HFT, Tabb Forum, 24 de agosto de 2010 A negociação de alta velocidade não é o progresso da pirataria, Bloomberg View, 12 de abril de 2012.

Cursos ensinados.

GB.3350 Sistemas de Informação Financeira FIS Syllabus GB.3351 Sistemas de Gestão de Risco Sistemas de Gestão de Risco Syllabus GB.3351 Gestão de Risco em Gestão de Risco de TI no Plano de Estudos de TI GB.2312 FinTech Risk Management (co-teach). Programa de gerenciamento de risco de Fin Tech.

Citações de imprensa recentes e aparições públicas.

New York Times, Financial Times, Wall Street Journal, American Banker, MIT Technology Review, The Atlantic Magazine, BBC World Service, Australian Public Radio, Securities Industry News, Reuters, Guardian UK, Advanced Trader, Wall Street e Tecnologia, Wall Street Letter e Nikkei Shimbun Japan, et al.

Nyu stern trading strategies and systems.

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Nova edição especial do Big Data Journal sobre "Big Data for Social Good"

CNBC: Velocidade - A única vantagem HFT? Não tão rápido.

Vasant Dhar nomeou editor-chefe dos grandes dados.

Ciência dos Dados e Previsão.

Financial Times: as escolas de negócios enfrentam um futuro desafiador.

CNBC: Como o Facebook pode monetizar seus dados.

Wired Magazine: receba os seus dados no Facebook.

Financial Times: lições de privacidade do roubo de dados da Sony.

Revista CIO: não gamble a reputação de sua empresa na governança de dados.

Forbes: Nova fase para a indústria editorial.

Vasant Dhar é um cientista de dados cuja pesquisa aborda a seguinte pergunta: quando os computadores tomam melhores decisões do que os humanos? Os interesses mais amplos de Dhar incluem dados e governança de TI.

A pesquisa de Dhar sobre a tomada de decisão baseia-se em Inteligência Artificial, Aprendizado de Máquinas e dados grandes e pequenos. As principais áreas problemáticas abordadas na pesquisa são finanças, saúde, educação, negócios e esportes. Na arena financeira, por exemplo, sua pergunta principal pergunta se você deve confiar em seu dinheiro para um robô. Por exemplo, veja aqui.

Perguntas semelhantes aplicam-se nas outras arenas. Por exemplo, os computadores poderiam fazer professores melhores do que humanos? Eles poderiam nos oferecer um conselho de saúde valioso que os especialistas não podem fornecer? Eles poderiam se tornar valiosos treinadores assistentes?

Há apenas cinco anos, teria parecido absurdo pensar que os computadores poderiam conduzir carros melhor do que os humanos em nossa vida, mas os carros sem motorista já estão aqui. Os computadores estão tomando mais e mais decisões para nós, e cada vez mais em áreas que exigem julgamento humano. Como podemos alavancar os rápidos avanços na inteligência das máquinas em áreas como finanças, saúde e educação?

Dhar ensina cursos sobre Marketing Digital, Estratégias de Negociação e Previsão.

O professor Dhar recebeu seu Bacharel em Tecnologia do Indian Institute of Technology em Delhi e seu Mestrado em Filosofia e Doutor em Filosofia pela Universidade de Pittsburgh.

Kenneth C. Abbott, instrutor desde 2004. Ken Abbott é diretor administrativo da Morgan Stanley em Nova York. Ele é responsável pela gestão de risco das moedas, das taxas de juros, das commodities e dos mercados emergentes. Ele também é responsável pela metodologia de risco de mercado e risco de crédito. Anteriormente, ele era vice-presidente sênior e chefe global de risco de mercado do banco de investimentos do Bank of America, incluindo atividades de negociação, validação de modelo, análise de crédito e treinamento quantitativo. Licenciado em Harvard em Economia, 1983. Mestrado em Economia pela NYU / GSAS em 1991, MS em Estatística e Pesquisa de Operações da NYU / Stern em 1994. Ele está no Conselho de Associação Global de Profissionais de Riscos.

Steve Allen, instrutor 1998-2009. Steve se aposentou do JPMorgan Chase em 2004, após uma carreira de 35 anos no setor financeiro, mais recentemente como diretor-gerente da JPMorgan Chase, responsável pela metodologia de risco, incluindo responsabilidade pela metodologia de capital para risco de mercado e de crédito, pesquisa em questões de risco, e revisão do modelo. As posições anteriores que ele ocupou incluem 7 anos como chefe de gerenciamento de risco de mercado para todos os produtos derivados da Chase em todo o mundo e 10 anos como diretor de modelagem para as atividades de negociação da Chase. Steve ensinou no programa Courant Masters em programas de Finanças Matemáticas desde 1998. Ele é o autor da Gestão de Riscos Financeiros: um Guia do Profissional para Gerenciamento de Risco de Mercado e Crédito e está no Conselho de Diretores da Associação Internacional de Engenheiros Financeiros.

Robert Almgren, instrutor 2006-2009. Co-fundador da Quantitative Brokers. Até 2008, o Dr. Almgren foi Diretor Gerente e Chefe de Estratégias Quantitativas no grupo de Serviços de Negociação Eletrônica do Banc of America Securities. De 2000 a 2005, foi professor titular de Matemática e Ciência da Computação na Universidade de Toronto e diretor do Programa de Mestrado em Finanças Matemáticas. Antes disso, foi professor adjunto de Matemática na Universidade de Chicago e Diretor Associado do Programa de Matemática Financeira. O Dr. Almgren possui um B. S. em Física e Matemática do Massachusetts Institute of Technology, um M. S. em Matemática Aplicada da Universidade de Harvard e um Ph. D. em Matemática Aplicada e Computacional da Universidade de Princeton. Ele possui um extenso registro de pesquisa em matemática aplicada, incluindo vários artigos sobre negociação de valores mobiliários ótimos, medição de custos de transações e formação de portfólio.

Leif Andersen, instrutor desde 2004. Leif B. G. Andersen é o co-chefe global do The Quantitative Strategies Group no Bank of America Merrill Lynch. Ele possui mestrado em Engenharia Elétrica e Mecânica pela Universidade Técnica da Dinamarca, um MBA da Universidade da Califórnia em Berkeley e um PhD em Finanças da Universidade de Aarhus Business School. Ele foi o co-destinatário do Prêmio Quant of the Year 2001 da Revista de Riscos e trabalhou há mais de 20 anos como pesquisador quantitativo na área de preços de derivativos. Ele é autor de artigos de pesquisa influentes e livros em todas as áreas de financiamento quantitativo, incluindo a monografia de três volumes "Taxa de juros Modelling" (co-autoria com Vladimir Piterbarg). Ele é editor associado do Journal of Computational Finance.

Farshid M. Asl, Ph. D, Diretor de Gerenciamento, Divisão de Gerenciamento de Investimentos, Goldman Sachs, Farshid atualmente é chefe da Alocação Estratégica e Quantitativa de Ativos no Grupo de Estratégia de Investimento, onde é responsável pela modelagem e análise quantitativa de táticas pontos de vista e alocações estratégicas de ativos. Anteriormente, ele passou dois anos como estrategista nas Rendas Fixas, Moeda e Mercadorias e nas Divisões de Patrimônio da Goldman Sachs, desenvolvendo modelos quantitativos de negociação de crédito e volatilidade. Farshid se juntou à Goldman Sachs em 2006 e foi nomeado diretor-gerente em 2011. Antes de se juntar à Goldman Sachs, Farshid foi vice-presidente sênior de Análise de Riscos da GMAC, onde se concentrou em direção estratégica e liderança no desenvolvimento e operações dos sistemas de gerenciamento de riscos e processos para GM, GMAC e GM Asset Management. Farshid é professor e professor adjunto do Instituto Courant de Ciências Matemáticas da Universidade de Nova York desde 2005. Ele publicou vários trabalhos em periódicos quantitativos e apresentou em conferências e liderou várias oficinas de negociação quantitativa. Farshid obteve um doutorado em Controle Otimista Estocástico e um MS em Engenharia Financeira pela Universidade de Michigan em 2002. Ele é o destinatário do Prêmio Carl Alessandre de Carl T. Humphrey 2005 da Universidade Villanova.

Marco M. Avellaneda, Professor de Matemática e Diretor da Divisão de Matemática Financeira, Organizador da Série de Seminários de Finanças Matemáticas. Instrutor desde 1998. B. S. Buenos Aires, 1981, Ph. D. Universidade de Minnesota, 1985. Os centros de pesquisa de Marco abordam estratégias de negociação quantitativas e modelos financeiros. Ele publicou em finanças matemáticas e matemática aplicada, incluindo modelagem de volatilidade, o design de materiais compósitos e turbulência hidrodinâmica. Ele era um V. P. em Morgan Stanley em 1997 e 1998 no Derivatives Products Group; Gerente de Carteira da Capital Fund Management, onde criou o Nimbus Fund 2004; Gerente de Carteira em um importante fundo de hedge de Nova York, onde executou Statistical Arbitrage 2006 a 2008; Parceiro da Financial Concepts LLC, uma consultoria de gerenciamento de riscos com escritórios em Nova York e Paris 2003 para apresentar; Editor de periódicos Quantitative Finance, International Journal of Theoretical and Applied Finance, onde foi editor-chefe de 1998 a 2003; Editor gerente do International Journal of Theoretical and Applied Finance; Editor Associado de Comunicações em Matemática Pura e Aplicada e Métodos Matemáticos em Ciências Aplicadas. Marco é o autor do livro "Modelagem Quantitativa de Valores Derivados: Da Teoria à Prática" e editou a coleção "Análise Quantitativa em Mercados Financeiros, Vols I - III". Marco foi denominado Quant do Ano 2010 pela revista Risk.

Peter Carr, Diretor Executivo do Programa S. S. em Matemática em Finanças, Instrutor desde 2003. Na primavera de 2010, Peter se juntou ao Morgan Stanley como Diretor Gerente e Cabeça Global de Modelagem de Mercado. Anteriormente, liderou o grupo de Pesquisa Financeira Quantitativa para Bloomberg em NY e grupos de pesquisa de derivativos de capital no Banc of America Securities e no Morgan Stanley. Suas posições acadêmicas incluem 4 anos como professor adjunto na Universidade de Columbia e 8 anos como professor de finanças na Universidade de Cornell. Peter recebeu seu doutorado. em Finanças da UCLA em 1989 e publicou extensivamente em revistas acadêmicas e orientadas para o setor. Ele é editor associado para 6 periódicos acadêmicos relacionados a finanças matemáticas e derivados e um orador freqüente em conferências profissionais e acadêmicas. Ele é a "Quant of the Year" da revista Risk, em 2003, e o prêmio da Wilmott Magazine para "Cutting Edge Research" em 2004.

Jan Dash, Instrutor desde 2009, dirige o Grupo de Pesquisa de Risco Estratégico da Bloomberg L. P. Ele foi Diretor e gerenciou grupos de risco / quantidade na Merrill Lynch, Eurobrokers, Fuji Capital Markets, Salomon Smith Barney / Citigroup e Moore Capital Management. Ele também é um estudante de pesquisa visitante da Fordham University's Graduate School of Business e presidente da J. Dash Consultants LLC. Jan introduziu integrais de caminho Feynman-Wiener para financiar como um paradigma geral. Ele inventou Stressed Value at Risk, uma medida de risco prático avançado para crises financeiras. Ele co-inventou o modelo Macro-Micro que produz uma descrição mais realista das variáveis ​​subjacentes, incluindo escalas de tempo longas e curtas. Ele escreveu o livro Quantitative Finance and Risk Management, A Physicist's Approach (World Scientific, 2004). Em sua carreira física anterior, foi Diretor de Pesquisa no Centro de Physique Theorique (CNRS, Marselha, França), MTS no Bell Labs e na faculdade da Universidade de Oregon. Ele publicou mais de 60 artigos científicos. Ele possui uma licenciatura da Caltech e um doutorado em física teórica de alta energia da UC Berkeley.

Bruno Dupire, instrutor desde 2005. Depois de ter liderado equipes de pesquisa de derivativos no Societe Generale, Paribas e Nikko FP, Bruno ingressou na Bloomberg em Nova York em 2004 para desenvolver análises avançadas. Ele é mais conhecido por seu trabalho sobre modelagem de volatilidade, incluindo o modelo de volatilidade local (1993), a extensão mais simples do modelo Black-Scholes-Merton para atender a todos os preços das opções e resultados subseqüentes em volatilidade estocástica e derivativos de volatilidade. Ele foi incluído em dezembro de 2002 na revista Risk "Hall of Fame" das 50 pessoas mais influentes na história dos derivados. Ele recebeu o prêmio Wilmott de "pesquisa de ponta" de 2006 e foi eleito em 2006 o mais importante profissional de derivativos dos últimos 5 anos na pesquisa do ICBI Global Derivatives.

Vladimir Finkelstein, instrutor desde 2005, é sócio fundador e diretor de ciência da Horton Point LLC, empresa de gestão de investimentos especializada em estratégias quantitativas em classes de ativos. Antes disso, ele era Diretor Gerente e Chefe de Pesquisa Quantitativa no Citadel Investment Group (2003-2005), e o Head Derivatives Risk Modeler e um diretor global de Análise de Derivados de Crédito da Goldman, Sachs (2000-2003). Vladimir iniciou sua carreira em finanças em J. P. Morgan em 1991, onde construiu o Grupo de Pesquisa de Derivados de Renda Fixa em Nova York e, posteriormente, foi responsável pela Global Credit Derivatives Analytics. Ele possui um Ph. D. em Física da NYU e um M. S. em Física Teórica do Instituto de Física e Tecnologia de Moscovo.

Eran Fishler é o Chief Operating Office da Pragma Securities, que ingressou em 2007. Ele lidera as equipes de tecnologia e pesquisa no desenvolvimento de novos e inovadores produtos e serviços de negociação algorítmica. Anteriormente, a Eran trabalhava na Hite Capital Management. Eran possui um Ph. D. em Engenharia Elétrica da Universidade Israelense de Tel Aviv e um MBA da Escola de Negócios Stern da Universidade de Nova York. Eran é um especialista em campo de estimação de parâmetros e teoria de detecção e publicou mais de 40 trabalhos técnicos na área de processamento de sinal estatístico.

Bjorn Flesaker é Diretor Gerente e Chefe de Pesquisa Quantitativa em Renda Fixa Prudencial, onde é responsável pela pesquisa e modelagem tanto para gerenciamento de risco quanto para fins de valor relativo. Antes de se juntar à Prudential em 2010, trabalhou na pesquisa de quant e gerenciamento de negócios de renda fixa na Bloomberg. Bjorn gerenciou grupos orientados a derivações para várias instituições, incluindo Morgan Stanley, Bear Stearns e Merrill Lynch, e foi professor adjunto de Finanças na Universidade de Illinois em Urbana-Champaign. Ele atualmente atua como Editor Gerente do International Journal of Theoretical and Applied Finance. Bjorn detém um primeiro grau da Norwegian School of Management (1985) e um doutorado em finanças pela University of California - Berkeley (1990).

Jonathan B. Goodman, Professor de Matemática; Presidente fundador do Comitê de Matemática em Finanças; Instrutor desde 2000. Jonathan ganhou seu Ph. D. em 1982 pela Universidade de Stanford, especializado em matemática computacional e aplicada. Os seus interesses de pesquisa variaram desde a teoria matemática das ondas de choque até métodos inovadores de Monte Carlo na química quântica. Sua consultoria privada incluiu trabalho sobre métodos computacionais em finanças para Morgan Stanley Co. e NumeriX.

Ali Hirsa, Instrutor desde 2004. Ali é sócio-gerente da Sauma Capital, LLC. Anteriormente, foi sócio e chefe de estratégia de negociação analítica na Caspian Capital Management, LLC. Antes de ingressar no Caspian, Ali trabalhou em uma variedade de posições "quantas" em Morgan Stanley, Banc of America Securities e Prudential Securities. Ele também é professor adjunto adjunto de engenharia financeira na Universidade de Columbia (desde 2000) e um colega no programa de matemática de finanças no Instituto Courant da Universidade de Nova York (desde 2004).

Ali é o autor de Computational Methods in Finance (Chapman Hall / CRC 2012), co-autor de Uma Introdução à Matemática de Derivados Financeiros (Academic Press 2013) e um editor do Journal of Investment Strategies. Além de muitas publicações em periódicos acadêmicos, Ali é um orador freqüente nas conferências acadêmicas e profissionais.

Ali recebeu seu doutorado em matemática aplicada da University of Maryland, no College Park, sob a supervisão dos professores Howard C. Elman e Dilip B. Madan. Ele atualmente atua como administrador da Fundação do Parque Universitário da Universidade de Maryland.

Brett Humphreys, Instrutor desde 2008. Brett é diretor executivo do grupo de commodities da Morgan Stanley, onde se concentra no gerenciamento de risco. Antes disso, ele trabalhou no grupo de commodities em J. P Morgan, como consultor de gerenciamento de risco da Price Waterhouse e em assessoria de gerenciamento de risco da Bankers Trust. Em 1999, co-fundou a Risk Capital, uma empresa independente de consultoria de gerenciamento de riscos que foi vendida em 2006 para a Towers Perrin. Ele possui uma licenciatura em Harvard em Ciências Físicas (1991) e um Ph. D. da Universidade Estadual da Pensilvânia em Economia de Mineração, (1996).

Alireza Javaheri, Instrutora desde 2011. é o Head of Equities Quantitative Research Americas em J. P. Morgan. Ele trabalha desde 1994 no campo da análise quantitativa de derivativos em vários bancos de investimento, incluindo Goldman Sachs e Citigroup. Ele segura um M. Sc. em Engenharia Elétrica do Instituto de Tecnologia de Massachusetts e uma Ph. D. em Finanças da Ecole des Mines de Paris. Ele também é um titular da carta patente CFA. É autor de vários documentos de financiamento quantitativos sobre o tema da volatilidade, incluindo artigos com Peter Carr, Paul Wilmott e Espen Haug. Seu livro "Inside Volatility Arbitrage" foi eleito o livro de finanças quantitativas do ano pela revista Wilmott.

Robert V. Kohn, Professor de Matemática; Presidente do Comitê de Matemática em Finanças (2003-2006 e 2009-2011); Instrutor desde 1998. Bob recebeu seu Ph. D. da Universidade de Princeton em 1979. Seus interesses de pesquisa incluem ciência de materiais, equações diferenciais parciais não-lineares, problemas inversos e otimização, bem como finanças.

Petter N. Kolm, Professor Associado Clínico de Matemática desde 2007; Diretor do Programa de Matemática em Finanças M. S. Os interesses de pesquisa da Petter incluem estratégias de negociação quantitativas, gerenciamento de portfólio delegado, econometria financeira, gerenciamento de riscos e estratégias ótimas de portfólio. Ele é membro do conselho editorial do Journal of Portfolio Management. Anteriormente, a Petter trabalhava no Grupo de Estratégias Quantitativas da Goldman Sachs Asset Management, onde suas responsabilidades incluíam pesquisar e desenvolver novas estratégias quantitativas de investimento para o hedge fund do grupo. Petter co-autorizou os livros de Modelagem Financeira do Mercado de Capitais: de CAPM para Cointegration (Wiley, 2006), Tendências em Finanças Quantitativas (CFA Research Institute, 2006) e Robust Portfolio Management and Optimization (Wiley, 2007). Ele é doutorado em matemática pela Universidade de Yale, um M. Phil. em matemática aplicada do Royal Institute of Technology em Estocolmo, e um M. S. em matemática da ETH Zurich.

Keith Lewis, instrutor desde 2008. Treinando, Keith é uma matemática que iniciou sua carreira como professora assistente na Brown University. Desde 1991, Keith trabalha em Nova York nas principais empresas de investimento. Na Bankers Trust, ele fez tecnologia para o grupo de derivativos de taxa de juros e trabalhou no grupo Quant responsável por todos os produtos derivados negociados por banqueiros em todo o mundo. No Morgan Stanley Keith trabalhou no grupo de tecnologia que apoia a mesa de derivativos de renda fixa, correu o lado técnico de sua subsidiária AAA e foi membro do grupo do Tesouro Global responsável pela determinação de encargos de capital para todas as negociações realizadas pelo Derivative Products Group. No Banc of America Securities Keith estava ativamente envolvido em muitos níveis de seu negócio de títulos derivativos. Desde 2002, a Keith vem fazendo consultoria para hedge funds e bancos de investimento.

Richard Lindsey, instrutor desde 2009, é presidente do Callcott Group, LLC, um grupo de consultoria especializado em mercados financeiros, gerenciamento de riscos e análise de portfólio quantitativo. Ele é o presidente da Associação Internacional de Engenheiros Financeiros. Até dezembro de 2006, o Dr. Lindsey foi presidente da Bear, Stearns Securities Corporation e membro do Comitê de Gestão das Bear Stearns Companies, Inc. Antes de se juntar a Bear Stearns, o Dr. Lindsey atuou como Diretor de Regulação de Mercado para os US Securities and Comissão de intercâmbio e como economista-chefe da SEC. Ele era professor de finanças na Yale School of Management antes de se juntar à SEC. O Dr. Lindsey realizou um extenso trabalho nas áreas de microestrutura do mercado e no preço de títulos derivados. Ele ocupou os cargos de Visiting Academic no Nikko Research Institute em Tóquio, Japão, e Visiting Economist na Bolsa de Valores de Nova York. Ele tem um B. S. em Engenharia Química do Illinois Institute of Technology, um M. S. em Engenharia Química de Berkeley, um M. B. A. da Universidade de Dallas, e um Ph. D. em Finanças da Universidade da Califórnia, em Berkeley.

Lee Maclin, Instrutor desde 2000. Lee tem mais de vinte anos de experiência em Wall Street e trabalhou e consultou algumas de suas maiores e mais conhecidas firmas. Desde 1991, a Lee trabalhou principalmente nos campos de comércio e gestão de investimentos, especializada na aplicação de métodos estatísticos, modelagem e simulação de alta freqüência. De 1993 a 1997, Lee dirigiu um departamento de negociação quantitativo para Mint Investment Management, que, na época, era um dos maiores consultores de comércio de commodities do mundo. Em 2002, Lee foi um dos sócios fundadores da Pragma Financial Systems e, durante os próximos seis anos, atuou como Diretor de Pesquisa. Na Pragma, o trabalho de Lee concentrou-se no desenvolvimento de ferramentas ótimas de execução e gerenciamento de portfólio dinâmico. Ele é um orador freqüente sobre o tema de negociação algorítmica e finanças computacionais.

Fabio Mercurio, Instrutor em 2011, é gerente de negócios da Bloomberg LP, Nova York. Anteriormente, ele era chefe da Engenharia Financeira da Banca IMI, em Milão. Possui Bacharelado em Matemática Aplicada pela Universidade de Pádua e Ph. D. em Finanças Matemáticas pela Universidade Erasmus de Roterdã. Seus recentes interesses científicos incluem modelos de taxa de juros e inflação para preços e hedge exotics, o preço dos híbridos e a modelagem do sorriso para diferentes classes de ativos. Fabio publicou extensivamente em livros e revistas internacionais, incluindo 10 artigos de ponta na revista Risk. Ele também criou em conjunto o livro 'Modelos de taxa de juros: teoria e prática'.

Robert Reider, Instrutor desde 2007, é um Gerente de Carteira para o Millennium Partners, um fundo de hedge multistrategy, onde ele desenvolve e comercializa várias estratégias quantitativas de equidade. Antes disso, ele era vice-presidente da J. P. Morgan no grupo Opções de câmbio (1994-1997) e do grupo de comércio proprietário (1997-2000). Ele possui um Ph. D. em Finanças da Wharton School e um BS e MS em Engenharia de Sistemas pela Universidade da Pensilvânia.

Gordon Ritter, instrutor desde 2013, é vice-presidente do grupo de arbitragem estatística da Highbridge Capital Management, onde já passou desde 2008. Antes disso, completou seu doutorado na Universidade de Harvard, onde publicou pesquisas originais em vários campos, incluindo quantum teoria do campo, computação quântica e álgebra abstrata. Ele é um destinatário do prêmio de Harvard pela excelência no ensino. Ele também possui Licenciatura em Honra da Universidade de Chicago em Matemática.

Glen Swindle, instrutor desde 2009. Glen Swindle é o sócio-gerente e co-fundador da Scoville Risk Partners, uma empresa global de serviços profissionais focada nos setores de energia e commodities. Glen ocupou cargos seniores na Constellation Energy, onde dirigiu o Grupo de Estratégias para o negócio de energia comercial e no Credit Suisse, onde, como Diretor Gerente e Co-Chefe de Energia e Comércio de Gás Natural, ele dirigiu equipes de negociação estruturadas responsáveis ​​por importantes aspectos do negócio de energia norte-americano. Anteriormente, ocupava cargos na UCSB e na Universidade Cornell. Ele atualmente ocupa um cargo adjunto de faculdade na Universidade de Nova York, onde ele fala sobre avaliação de energia e gerenciamento de portfólio. Ele também está no Comitê de Supervisão de Energia para o Programa Profissional de Risco Energético do GARP e é um orador freqüente em discussões em painel e webinars. Glen é o autor de Avaliação e Gestão de Riscos em Mercados de Energia (Cambridge University Press, 2014). Ele possui um Ph. D. em Matemática Aplicada da Universidade de Cornell, um M. S. E. em Engenharia Mecânica Aeroespacial de Princeton, e um B. S. em Engenharia Mecânica da Caltech.

Leon Tatevossian, instrutor desde 2009, atualmente é diretor da equipe de Gerenciamento de Risco do Grupo da RBC Capital Markets, LLC, onde trabalhou desde 2009. Na RBC, ele cobre o risco de mercado de segurança garantida por ativos (ABS) e hipotecário comercial negociação de segurança (CMBS). Leon tem vinte e seis anos de experiência nos mercados de capitais de renda fixa, incluindo cargos como comerciante, estrategista quantitativo, modelista de derivativos e analista de risco de mercado. Em 2006-07, ele era um comerciante principal e sénior no MBS / ABS principal - grupo de investimento do Banc of America Securities. O fundo do produto de Leon inclui títulos do Tesouro dos EUA, títulos da agência dos EUA, derivativos de taxa de juros, MBSs e derivativos de crédito. Ele se formou no MIT (SB, matemática) e era um estudante de pós-graduação em matemática (teoria dos números algébricos) na Brown University.

Para ganhar um Mestrado em Engenharia Financeira, os alunos devem completar 33 créditos para se qualificarem para a graduação. A estrutura do programa é a seguinte:

Os alunos podem escolher entre uma das seguintes faixas:

Finanças corporativas e mercados financeiros.

Tecnologia e Finanças Algorítmicas.

Os alunos podem solicitar uma trilha personalizada. Por favor, reveja as diretrizes aqui.

Os alunos também devem completar o Programa de treinamento on-line Bloomberg Essentials e obter o Reconhecimento de conclusão para se qualificar para a graduação. O Departamento apoiará seus esforços para completar o programa de treinamento, fornecendo muitos terminais da Bloomberg e assistentes de laboratório para responder suas perguntas. Este é um requisito de crédito zero, listado como FRE 5500.

Os estudantes de graduação matriculados em outros programas de pós-graduação da NYU podem solicitar a inscrição em cursos de FRE por até 6 créditos por semestre com a aprovação do seu conselheiro de pós-graduação. Os alunos de graduação não têm permissão para fazer cursos no programa de MS em Engenharia Financeira, exceto para aqueles em um programa BS / MS combinado. É responsabilidade dos alunos consultar o seu consultor acadêmico se os cursos que eles planejam realizar satisfaçam os requisitos de licenciatura em seu programa e obter aprovação para inscrever-se em cursos de Engenharia Financeira através do formulário de registro cruzado FRE disponível na página Recursos Estudantis. Por favor, reveja a política de inscrição pré-escolar da NYU antes de enviar solicitações de registro cruzado.

CURSOS BÁSICOS (15 CRÉDITOS)

3 Créditos Contabilidade financeira FRE-GY 6003 Este curso fornece uma base sólida na construção e interpretação de demonstrações financeiras. Os tópicos incluem terminologia contábil; preparação e análise de demonstrações financeiras; Rácios de risco de liquidez e crédito; cálculos de depreciação; reconhecimento de receita; e passivos acumulados e avaliação de ativos. Também estão cobertos os efeitos das transações de capital; fluxos de caixa; e vários métodos contábeis nas demonstrações financeiras.

Pré-requisito: Pós-graduação. Co-Requisito: Nenhum. Notas: Nenhum. 3 Credits Economic Foundations in Finance FRE-GY 6023 This course studies the interactions between money, the financial system and the economy. Topics include supply and demand; consumer theory; theory of the firm; production costs and other subject areas such as interest rates and asset returns. This course summarizes key insights from financial economics as the methodological and conceptual basis of financial engineering.

Prerequisite: Graduate Standing 3 Credits Quantitative Methods in Finance FRE-GY 6083 This course focuses on quantitative methods and financial modeling. Probability theory, stochastic processes and optimization are studied and applied to a broad variety of financial problems and their derivatives. Topics include probability spaces; conditional probability; densities; distributions; density estimators; multivariate probability; moment-generating functions; random walks; Markov processes; Poisson processes; and the Brownian-motion process.

Prerequisite: Students are expected to know calculus and elementary probability and Graduate Standing 3 Credits Corporate Finance FRE-GY 6103 The modern corporation, as issuer of financial securities and end-user of financial risk-management products, is a major participant in financial markets and the economic counterpart to investors and financial intermediaries. The mechanism of financial markets and the valuation of instruments are studied in further detail in other courses. However, this course applies the tools of the trade of financial economics and corporate finance to the financial decision-making process of firms. Upon successful completion of this course, students know how to contribute to optimal financial decisions in a corporation: valuation; capital budgeting; risco; capital structure; dividend policy; long-term financing; gerenciamento de riscos; e fusões e aquisições. Increasingly important international factors that affect corporate finance are stressed throughout.

Prerequisite: Graduate Standing 3 Credits Financial Risk Management and Asset Pricing FRE-GY 6123 This course introduces the techniques and problems of Financial Risk Management and Asset Pricing. It emphasizes risk finance and attitudes; Value at Risk; risk measurement principles; valuation and expected utility and their relevance in the valuation and the pricing of financial investments; insurance; management of derivatives; and risk management. Throughout, risk-management application problems are explored. The course introduces and focuses on the fundamental principles of the Arrow-Debreu state preference theory used to price derivatives and other assets in complete markets. Risk neutral-Binomial models in option pricing; essential elements of Ito calculus; and the Black-Scholes model for pricing options are introduced and applied to practical financial decision making and risk management problems. Prerequisite: Graduate Standing.

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TRACK-REQUIRED COURSES (7.5 CREDITS)

Financial Markets and Corporate Finance Track.

Prerequisites: FRE-GY 6103 and FRE-GY 6003.

3 of the Following Courses:

1.5 Credits Financial Econometrics FRE-GY 6091 This course focuses on the art and science of statistical modeling of processes applied to business, finance and economics. These may include models of aggregate economic activity, economic behavior of firm or behavior of financial assets. Topics include statistical inference; maximum likelihood estimation; method of moments; Bayesian estimation; least-squares estimation; robust estimation; kernel estimation; copula estimation; analysis of variance; linear regression models; multiple regression; logistic regression; quantile regression; time series estimation; unit root tests; bootstrapping.

Prerequisite: FRE-GY 6083. Students are expected to know basic statistics. Prerequisite FRE-GY 6083 and Graduate Standing 1.5 Credits Econometrics and Time Series Analysis FRE-GY 6351 Financial econometrics has matured into a necessary and essential part of financial engineering that provides opportunities to deal with real and practical problems in finance. For example, techniques such as ARCH and GARCH and their subsequent development are used to estimate the volatility of underlying financial processes; the analysis of intra-day trading data that requires particular models and techniques; memory-based and fractal stochastic processes to study complex markets behaviors and copulas applied routinely to model - and estimate-dependent risks. These financial and risk problems require the application of advanced financial-econometric techniques, which the course provides from both theoretical and empirical-applied viewpoints. Selected cases provide a real-world sense of financial engineering when it is faced with financial-market reality and complexity.

Prerequisite: FRE-GY 6083 and Graduate Standing 1.5 Credits Corporate and Financial Strategy FRE-GY 6361 This is an introduction to financial strategy for MS Financial Engineering students. The course focuses on the role of financial engineers and financial officers in developing and sustaining competitive advantage through the use of financial engineering analyses.

Prerequisites: Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of the Department FRE-GY 6023 and FRE-GY 6103. 1.5 Credits Mergers & Acquisitions FRE-GY 6391 This course examines the theories and empirical evidence related to mergers and acquisitions and other corporate transactions and reorganizations. The course looks at friendly mergers, hostile takeovers (including takeover and anti-takeover tactics), leveraged buyouts and bankruptcy. Throughout, the course examines the motives behind these transactions and reorganizations.

Prerequisites: FRE-GY 6103 and Graduate Standing 1.5 Credits Fixed Income Securities and Interest Rate Derivatives FRE-GY 6411 This course examines the body of analytical tools and measures that constitute modern fixed-income markets. The valuation of interest-rate sensitive cash flows is the unifying theme. Major topics include theories of term structure, institutional aspects of fixed-income markets and analytical techniques for managing interest-rate risk. Bond refunding, defeasance, corporate bonds, forwards, futures, options and interest-rate swaps are discussed. The course gives an overview of the major classes of fixed-income securities and the markets in which they trade. Among the major classes of fixed-income instruments discussed are Treasury and agency securities, mortgage-backed securities (including CMOs and Strips), asset-backed securities, municipals, floating and inverse floating rate securities.

Prerequisite: FRE-GY 6023, FRE-GY 6083, FRE-GY 6103 and Graduate Standing 1.5 Credits Global Finance FRE-GY 6671 The level of economic and financial globalization combined with the growth of the multinational firms and virtual firms with no boundaries may have altered the future of finance and its risk engineering. The purpose of this course is to focus attention on the essential elements that both large financial firms and institutions are confronting worldwide, the challenges of national and international financial investments, currencies speculations and investments, regulation as well as managing risks in a strategic and macroeconomic environment. In such an environment, financial markets are multi-polar, geographically distributed with national entities pursuing their own economic and political agenda.

Prerequisites: FRE-GY 6411 and FRE-GY 6511 and matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of the Department 1.5 Credits Quantitative Portfolio Management FRE-GY 6711 This course focuses on the quantitative foundations of portfolio management. It teaches the fundamental mathematical models such as the Markowitz, CAPM, and the Merton investment-consumption models, and discusses the issues related to the implementation of these models in practice to different types of portfolios. Finally, it also introduces some common portfolio construction and rebalancing techniques.

Prerequisites: Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of the Department FRE-GY 6083.

Computational Finance Track.

3 Credits Dynamic Assets and Option Pricing FRE-GY 6303 This course provides the foundations of option pricing models. The problems are either solved analytically by the martingale and Partial Differential equation approaches, or numerically, by applying approximation and simulation methods. The applications to both European and American options, exotic options, and bonds will be presented. Since the same techniques allow the treatment of more complex financial products, application to fixed income derivatives such as interest rate caps will also be presented. This course is a requirement in the Computational Finance Track and is a track elective in the Risk Finance Track.

Prerequisites: Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of Department FRE-GY 6083.

3 of the Following Courses:

1.5 Credits Extreme Risk Analytics FRE-GY 6041 The course covers failures of financial theory in risk management, deriving from fundamental definitions and assumptions in modeling, including pricing formulae; convexity; stochasticity and volatility; fat tails; e risco. Other topics: Portfolio robustness and extreme markets and moral hazard; data-mining biases and decision error; and decision-making with incomplete information.

Pre-Requisite: Graduate Standing 1.5 Credits Financial Econometrics FRE-GY 6091 This course focuses on the art and science of statistical modeling of processes applied to business, finance and economics. These may include models of aggregate economic activity, economic behavior of firm or behavior of financial assets. Topics include statistical inference; maximum likelihood estimation; method of moments; Bayesian estimation; least-squares estimation; robust estimation; kernel estimation; copula estimation; analysis of variance; linear regression models; multiple regression; logistic regression; quantile regression; time series estimation; unit root tests; bootstrapping.

Prerequisite: FRE-GY 6083. Students are expected to know basic statistics. Prerequisite FRE-GY 6083 and Graduate Standing 1.5 Credits Numerical & Simulation Techniques in Finance FRE-GY 6251 Advanced numerical techniques for the solution of ordinary, partial and stochastic differential equations are presented. These techniques are analyzed both mathematically and using computer aided software that allows for the solution and the handling of such problems. In addition, the course introduces techniques for Monte Carlo simulation techniques and their use to deal with theoretically complex financial products in a tractable and practical manner. Both self-writing of software as well as using outstanding computer programs routinely used in financial and insurance industries will be used.

Prerequisite: FRE-GY 6083 and Graduate Standing 1.5 Credits Financial Risk Management and Optimization FRE-GY 6331 This course provides solutions to the inter-temporal problems in financial management of portfolios, credit risks and market making. Dynamic and stochastic dynamic programming techniques as well as optimal control and stochastic control principles of optimality are presented, and their financial contexts emphasized. Both theoretical and practical facets of inter-temporal management of financial risks and risk pricing are also stressed. The course uses financial and optimization software to solve problems practically.

Prerequisites: FRE-GY 6083, FRE-GY 6123, and FRE-GY 6091 and Graduate Standing. 1.5 Credits Econometrics and Time Series Analysis FRE-GY 6351 Financial econometrics has matured into a necessary and essential part of financial engineering that provides opportunities to deal with real and practical problems in finance. For example, techniques such as ARCH and GARCH and their subsequent development are used to estimate the volatility of underlying financial processes; the analysis of intra-day trading data that requires particular models and techniques; memory-based and fractal stochastic processes to study complex markets behaviors and copulas applied routinely to model - and estimate-dependent risks. These financial and risk problems require the application of advanced financial-econometric techniques, which the course provides from both theoretical and empirical-applied viewpoints. Selected cases provide a real-world sense of financial engineering when it is faced with financial-market reality and complexity.

Prerequisite: FRE-GY 6083 and Graduate Standing 1.5 Credits Credit Risk & Financial Risk Management FRE-GY 6491 This course provides an overview and analysis of the market for debt obligations of state and local governments. Topics will include the micro structure of the market, including the types of debt issued, and characteristics of the buyers. Federal and state taxation of munis will be discussed, along with industry regulatory structure. Bond structure, risk assessment and risk management using cash bonds, futures and options will be covered.

Prerequisites: FRE-GY 6411 and Graduate Standing.

Technology and Algorithmic Finance Track.

3 Credits Foundations of Financial Technology FRE-GY 6153 Financial Institutions spend billions per year to exploit the latest development in information technology. This course introduces a framework with which to understand and leverage information technology. The technology components covered include telecommunications, groupware, imaging and document processing, artificial intelligence, networks, protocols, risk, and object-oriented analysis and design. the course also covers the entire technological-planning process specifically for financial institutions.

Prerequisite: Graduate Standing.

3 of the Following Courses:

1.5 Credits Extreme Risk Analytics FRE-GY 6041 The course covers failures of financial theory in risk management, deriving from fundamental definitions and assumptions in modeling, including pricing formulae; convexity; stochasticity and volatility; fat tails; e risco. Other topics: Portfolio robustness and extreme markets and moral hazard; data-mining biases and decision error; and decision-making with incomplete information.

Pre-Requisite: Graduate Standing 1.5 Credits Clearing and Settlement and Operational Risk FRE-GY 6131 This course focuses on issues involved in processing financial transactions—from order execution to final settlement of transactions—and operational risk in general. The course examines the procedures and market conventions for processing, verifying, and confirming completed transactions; resolving conflicts; decisions involved in developing clearing operations or purchasing clearing services; the role played by clearing houses; and numerous issues associated with cross-border transactions. The course also examines the effects of transaction processing, liquidity management, organizational structure, and personnel and compliance on the nature of operational risk. Qualitative and quantitative measures of operational risk are discussed.

Prerequisite: FRE-GY 6151 and Graduate Standing 1.5 Credits Numerical & Simulation Techniques in Finance FRE-GY 6251 Advanced numerical techniques for the solution of ordinary, partial and stochastic differential equations are presented. These techniques are analyzed both mathematically and using computer aided software that allows for the solution and the handling of such problems. In addition, the course introduces techniques for Monte Carlo simulation techniques and their use to deal with theoretically complex financial products in a tractable and practical manner. Both self-writing of software as well as using outstanding computer programs routinely used in financial and insurance industries will be used.

Prerequisite: FRE-GY 6083 and Graduate Standing 1.5 Credits Behavioral Finance FRE-GY 6451 This course discusses investors’ systematic deviations from the level of financial rationality assumed by modern financial theory. Such biased behavior can lead to market inefficiencies, market opportunities and market failure. After a brief introduction to the topic and its research history, the course focuses on the limits to arbitrage created by decision bias, the equity premium puzzle, market over-reaction and under-reaction. The course seeks to understand how and where opportunities for and threats to wealth accumulation exist as a result of the mismatch between investor behavior and the algorithmic assumptions about investment behavior inherent in financial theory.

Prerequisite: FRE-GY 6023 and Graduate Standing. 1.5 Credits Statistical Arbitrage FRE-GY 7121 Statistical arbitrage refers to strategies that combine many relatively independent positive expected value trades so that profit, while not guaranteed, becomes very likely. This course prepares students to research and practice in this area by providing the tools and techniques to generate and evaluate individual trading strategies, combine them into a coherent portfolio, manage the resulting risks, and monitor for excess deviations from expected performance. It introduces theoretical concepts such as cointegration, risk capital allocation, proper backtesting, and factor analysis, as well as practical considerations such as data mining, automated systems, and trade execution. Programming languages such as R, Python, or C++ will be used to present applications to data at low, intermediate and high frequency.

Prerequisites: Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of the Department FRE-GY 6123 and FRE-GY 6083 1.5 Credits Forensic Financial Technology and Regulatory Systems FRE-GY 7211 The goal of this course is to understand the technology behind financial forensics and regulatory systems. These include innovative database techniques (dataveillance), artificial intelligence, data mining, and non-parametric outlier methods used by the Secuities Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), as well as the FBI, and other federal and state agencies. Student teams will prepare and present projects or case studies applying hte concepts covered in class.

Prerequisite: FRE-GY 6151 and Graduate Standing 1.5 Credits Big Data in Finance FRE-GY 7221 This is an advanced course on practical computer science and database topics most relevant to financial applications. As such it covers fundamental concepts such as financial database design, use, and maintenance, distributed financial computing and associated storage, grid and cloud computing, modeling unstructured financial data, and data mining for risk management.

Prerequisite: FRE-GY 6151 and Graduate Standing 1.5 Credits Algorithmic Portfolio Management FRE-GY 7241 This course focuses on portfolio construction and rebalancing strategies such as momentum, value, and size strategies, among others. The course emphasizes backtesting and risk factor analysis as well as optimization to reduce tracking error. It will also address how a quantitative investment approach can help both individual and institutional investors make sound long-term investment decisions.

Prerequisite: FRE-GY 6123 and Graduate Standing 1.5 Credits Algorithmic Trading & High-Frequency Finance FRE-GY 7251 Algorithmic trading refers to the utilization of special computer programs in an order management system that restructure an order into a sequence of sub-orders based on the dimensions of submission time, price, size, and side. The goal of this course is to survey several algorithmic strategies used by financial institutions and to understand their implementation in the context of order management systems and standard financial protocols (such as FIX and FIXatdl). Student teams will prepare and present projects or case studies applying the concepts covered in class.

Prerequisites: FRE-GY 6151 and FRE-GY 7221 and Graduate Standing 1.5 Credits News Analytics & Strategies FRE-GY 7261 The fast-growing field of news analytics requires large databases, fast computation, and robust statistics. This course introduces the tools and techniques of analyzing news, how to quantify textual items based on, for example, positive or negative sentiment, relevance to each stock, and the amount of novelty in the content. Applications to trading strategies are discussed, including both absolute and relative return strategies, and risk management strategies. Students will be exposed to leading software in this cutting-edge space.

Prerequisites: FRE-GY 6151 and FRE-GY 7221 and Graduate Standing.

Risk Finance Track (Credit Risk, Financial Management, and Insurance)

7.5 Credits from:

1.5 Credits Extreme Risk Analytics FRE-GY 6041 The course covers failures of financial theory in risk management, deriving from fundamental definitions and assumptions in modeling, including pricing formulae; convexity; stochasticity and volatility; fat tails; e risco. Other topics: Portfolio robustness and extreme markets and moral hazard; data-mining biases and decision error; and decision-making with incomplete information.

Pre-Requisite: Graduate Standing 1.5 Credits Insurance Finance and Actuarial Science FRE-GY 6051 This course highlights essential facets of actuarial science, insurance and the finance-insurance convergence. The course assumes that students are familiar with basic notions of expected utility and stochastic processes, and options pricing. Topics include Insurance Business and Insurance Firms Management; Principles of Actuarial Science and Risk Pricing in Insurance and in Finance (Complete Markets); Expected Utility Approach to Insurance Risk Pricing and Management; Derivatives and the Financial Approach to Insurance Pricing; Insurance Products (Life Insurance, Casualty, Pension Funds and Defined Benefits); Principles of Insurance Management in a Dynamic and Global Setting. Throughout, the course uses numerous cases centered on actuarial and insurance problems and analyzes them from a financial perspective. Of particular interest are those related to insurance pricing, reserve policies, insurance pension funds, CATBOND and weather (insurance) derivatives and regulation.

Prerequisite: FRE-GY 6103. Co-Requisite: None. Notes: None. 1.5 Credits Clearing and Settlement and Operational Risk FRE-GY 6131 This course focuses on issues involved in processing financial transactions—from order execution to final settlement of transactions—and operational risk in general. The course examines the procedures and market conventions for processing, verifying, and confirming completed transactions; resolving conflicts; decisions involved in developing clearing operations or purchasing clearing services; the role played by clearing houses; and numerous issues associated with cross-border transactions. The course also examines the effects of transaction processing, liquidity management, organizational structure, and personnel and compliance on the nature of operational risk. Qualitative and quantitative measures of operational risk are discussed.

Prerequisite: FRE-GY 6151 and Graduate Standing 1.5 Credits Financial Market Regulation FRE-GY 6211 This course considers the role and forms of regulation in the U. S. financial markets, the role of the Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC), the Federal Reserve, the Office of the Controller of the Currency (OCC), and self-regulating organizations (SROs) such as the National Association of Securities Dealers and the National Futures Association. Also examined are the roles of the state insurance commissions and the STATE OR FEDERAL Department of Labor.

Prerequisites: FRE-GY 6031 and Graduate Standing 1.5 Credits Financial Risk Management and Optimization FRE-GY 6331 This course provides solutions to the inter-temporal problems in financial management of portfolios, credit risks and market making. Dynamic and stochastic dynamic programming techniques as well as optimal control and stochastic control principles of optimality are presented, and their financial contexts emphasized. Both theoretical and practical facets of inter-temporal management of financial risks and risk pricing are also stressed. The course uses financial and optimization software to solve problems practically.

Prerequisites: FRE-GY 6083, FRE-GY 6123, and FRE-GY 6091 and Graduate Standing. 1.5 Credits Credit Risk & Financial Risk Management FRE-GY 6491 This course provides an overview and analysis of the market for debt obligations of state and local governments. Topics will include the micro structure of the market, including the types of debt issued, and characteristics of the buyers. Federal and state taxation of munis will be discussed, along with industry regulatory structure. Bond structure, risk assessment and risk management using cash bonds, futures and options will be covered.

Prerequisites: FRE-GY 6411 and Graduate Standing.

Watch videos to learn more about these courses:

APPLIED LAB (1.5 CREDITS)

Students from all tracks except Risk Finance must choose 1 lab from the following:

1.5 Credits Financial Software Laboratory FRE-GY 6811 This course teaches students to use financial software tools commonly employed in industry. Examples include: Risk, Yieldbook, Excel, R, and C++.

Prerequisites: Graduate Standing 1.5 Credits Financial Econometric Laboratory FRE-GY 6821 This course teaches students to use Eviews and Stata.

Prerequisites: Graduate Standing 1.5 Credits Computational Finance Laboratory FRE-GY 6831 This course teaches students to use Matlab and GAMS.

Prerequisites: Graduate Standing 1.5 Credits Financial Software Engineering Laboratory FRE-GY 6861 This financial lab requires students to publicly participate in a large software project. This participation could take the form of contributing to an open-source financial software project with the contributions being accepted and committed to the main branch, or publishing a stand-alone library or package for a programming language commonly used in financial applications, or the development or updating of a brand-new industrial strength financial software application. As the students work on their project, this course will focus on important software engineering considerations specifically as they apply to the fast-paced world of financial projects, such as formalized procedures for revision control and bug tracking and other proven methods of software management in a fast-paced financial environment.

Prerequisite: Graduate Standing 1.5 Credits R in Finance FRE-GY 6871 This course introduces the free programming language R and its many applications to finance including risk management, portfolio construction, strategy development and testing, and trading and execution. Topics covered include financial time series analysis, advanced risk tools, applied econometrics, portfolio management, and derivatives valuation. Students will be required to write some code in R every week.

Prerequisites: Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of the Department FRE-GY 6123 and FRE-GY 6083 3 Credits Financial Computing FRE-GY 6883 This course covers programming applications to financial engineering, including C++ and Java and the various common development environments for them. Topics include structured and object-oriented programming in C++ with applications to binomial options pricing, multi-threaded programming in Java with applets and graphical interfaces with applications to risk measurement tools, data-based manipulation and programming in SQL and standard database access libraries with applications to historical financial data series retrieval and management, and other advanced programming concepts important for financial engineering such as numerical techniques, trading systems, and large-scale software design.

Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of the Department.

Note: Waivers are possible.

REQUIRED CERTIFICATION (0 CREDITS)

Bloomberg Certification FRE-GY 5500 This course tracks the requirement for the self-paced, self-taught Bloomberg certification to be completed through a Bloomberg terminal.

Prerequisite: Graduate Financial Risk Engineering students only.

CAPSTONE (3 CREDITS)

Choose 1 capstone option:

3 Credits Financial Engineering Capstone: Internship FRE-GY 7023 In this course, the Career Services Office helps the student to secure an internship. Students work under faculty supervision. However, the course is intended to be largely self-directed within the guidelines established by the supervising faculty member. A paper based on the internship work is required.

Prerequisites: This course should be taken during the student’s final semester. Prerequisites vary depending on the student’s track and the nature of the internship and Graduate Standing (200 hours at least; 2 reports to the faculty are required)

3 Credits Financial Engineering Capstone: Project FRE-GY 7043 In this project course, students work with faculty on proprietary or non-proprietary research projects. Generally, students work under faculty supervision. However, the course is intended to be largely self-directed within the guidelines established by the supervising faculty member. A significant written research component is required.

Prerequisites: This course should be taken during the student’s final semester. Prerequisites vary depending on the student’s track and the nature of the project to be undertaken and Graduate Standing. (project under faculty supervision)

3 Credits MS Thesis in Finance & Risk Engineering FRE-GY 9973 In this research course, students undertake proprietary or non-proprietary research and write a thesis-type research paper. Generally, students work under faculty supervision. However, the course is intended to be largely self-directed within guidelines established by the supervising faculty member.

Prerequisites: Graduate Standing. This course should be taken during the student’s final semester. Prerequisites vary depending on the student’s track and the nature of the thesis project.

2 special topics courses of 1.5 credits each, with a capstone paper submitted to the faculty.

CAPSTONE ASSESSMENT (0 CREDITS)

Capstone Assessment FRE-GY 5990 The Master of Science in Financial Engineering program offers four types of Capstone experiences to its graduate students: theses, projects, special topics, and internships. This Capstone Assessment will serve as a centralized measure for the various types of Capstone experiences to identify whether students have successfully completed this experience and garner feedback about graduating students' skills and professional readiness. Note: course should be completed during final semester of studies.

Prerequisites: FRE-GY 9973 or FRE-GY 7023 or FRE-GY 7043 or two special topics courses of 1.5 credits each, with a capstone paper submitted to the faculty.

GENERAL ELECTIVES (6 CREDITS)

Students may choose from any FRE courses outside of their specific track to fulfill these elective requirements or they may choose from the below general elective courses. They may also elect to register for up to three (3) classes (maximum of one 1 per semester) at select schools/programs at NYU. Students may only enroll for courses at other schools of NYU that are not offered at the School of Engineering. Please review the NYU cross-school registration policy prior to submitting cross-registration requests.

1.5 Credits Money, Banking and Financial Markets FRE-GY 6031 Studies how the interactions among money, the financial system and the economy determine interest rates and asset returns. It utilizes a consistent approach based in economics to explain the role of the financial system in matching savers and borrowers and in providing risk-sharing, liquidity and information services in efficient financial markets. Students study why and how financial markets and financial instruments evolve as a function of transactions and information costs, adverse selection and moral hazard problems, and summarize economic arguments for and against regulation. Finally, they examine the money supply process and monetary policy, in particular the link between monetary authorities and the macro-economy through a transmission mechanism involving banks and the non-financial public.

Prerequisite: Matriculation into a graduate program sponsored by the Department of Finance Risk Engineering, or permission of Department 1.5 Credits Investment Banking and Brokerage FRE-GY 6111 This course introduces an overview of Wall Street, the back office and general brokerage operations, investment banking and capital markets. The course covers subjects essential to understanding how products, once created, are distributed and sold. The course relies heavily on The Wall Street Journal, Financial Times and other trade publications. Topics include a brief history of Wall Street, an understanding of the major securities laws and how they have changed over time, basics of equity and debt securities, creation of debt and equity securities, and pricing and sale of debt and equity securities. The course seeks to understand how and where opportunities for creating new securities arise.

Prerequisites: Graduate Standing 1.5 Credits Applied Derivative Contracts FRE-GY 6291 This course provides an introduction to derivative contracts with a special emphasis on current practical applications in use today by financial institutions for investing, hedging, trading and issuing. The characteristics and features of futures, forwards, swaps, options and structured notes are all covered with a special emphasis on useful applications. For each of the four primary derivative contracts, we review in these lectures the appropriate definitions, terminology, market mechanics and theoretical fair value pricing.

Prerequisite: FRE-GY 6003, FRE-GY 6023, FRE-GY 6103 and Graduate Standing 1.5 Credits Derivatives Algorithms FRE-GY 6511 This course focuses on the algorithms behind derivatives valuation and applications. The focus is on the principles and practice of financial engineering and risk management and on developing intuition: understanding the reasons for the existence of the product, simulating possible paths and possible parameter values as an exploratory process, approximating complex derivatives as a combination of simpler ones, and attempting to replicate the payout. The goal is to prepare you to be able to evaluate an arbitrary derivative given only its term sheet. To that end, the course requires a project almost every week. Projects can be done in any programming language (Excel, Mathematica, R, Python, etc.), but the final result must be stand-alone tables and graphs. The primary prerequisite is familiarity with standard option pricing and Greeks. A portion of the final exam may involve a live computation project.

Prerequisite: FRE-GY 6123 and Graduate Standing 1.5 Credits Asset-Backed Securities and Securitization FRE-GY 6571 This course examines essential contributions in this field and provides a comprehensive coverage of financial securitization and their application to major asset-backed securities, structuring issues and relative value analysis. Topics include the expanding frontiers of asset securitization; introduction to ABS accounting; trends in the structuring of ABSs; and prepayment nomenclature in the ABS market.

Prerequisites: FRE-GY 6411, FRE-GY 6511 and Graduate Standing 1.5 Credits Basel 3 & Banking Assets Management FRE-GY 6731 This course addresses financial risk management and particularly focuses on Basel 3 directives and Value at Risk (VaR), a method to assess risk that employs standard statistical techniques routinely used in other fields. VaR analysis is used by bank and corporate managers and by financial market regulators.

Co-requisite: FRE-GY 6711 and Graduate Standing. 1.5 Credits Special Topics in Financial Engineering FRE-GY 6971 Current topics of particular importance in finance and risk engineering are analyzed and discussed. Selected topics will be emphasized and provide focus for further study. Examples might include urban finance engineering, environmental finance, infrastructure and projects finance, real estate finance, insurance finance and derivatives, macro hedge funds management, among others. Prerequisites: advanced standing and instructor’s permission and Graduate Standing 1.5 Credits Topics in Finance and Financial Markets I FRE-GY 7801 Current topics of particular importance in finance and risk engineering are analyzed and discussed. Selected topics are emphasized and provide focus for further study. Examples might include Financial Economics, Macroeconomics and Finance, the Bond market, the securities markets, Derivatives markets, Contract Theory, Credit and Counterparty Risks, Banking Finance and others.

Prerequisites: Graduate standing and instructor’s permission 1.5 Credits Topics in Risk Finance I FRE-GY 7821 Current top.

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7-Currency_Trading_Lecture NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Currency Trading Strategies Vasant Dhar* Professor Vasant Dhar 2016 Stern School of Business & NYU Center for Data Science / Editor-in-Chief, Big Data vasantdhar Currencies: The Big Picture Recap: S Register Now.

3-Trend following systems lecture NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Trading Strategies and Systems: Trend Following Systems Vasant Dhar* Professor Vasant Dhar 2016 Stern School of Business & NYU Center for Data Science / Editor-in-Chief, Big Data vasantdhar The Theo Register Now.

3-Trend following systems lecture.

2-Lectur_Measurement_Basics NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Trading Strategies and Systems: Measurement Vasant Dhar* Professor Vasant Dhar 2016 Stern School of Business & NYU Center for Data Science / Editor-in-Chief, Big Data vasantdhar Skill and Luck Skill Register Now.

Comprehensive case (1) NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Register Now.

Comprehensive case (1)

leadership NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Situational influence on ethical leadership: There are situations that impact followers appreciation of a leader as an ethical leader. Each situation is also an opportunity for an ethical leader to le Register Now.

leader conclusion NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Conclusion: In conclusion, a leader is a person who attracts people by his speech, actions or decisions. A leader with high in moral values, believes in ethics as a human himself follows them deeply i Register Now.

case management NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 The five management systems in an experiment are given in the case study as Traditional management, Share absence and sick leave, Share sales and inventory, Share information and brainstorm and Brains Register Now.

During the earlier days employees have lot of privacy at work place compare to todays privacy at wor NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 During the earlier days employees have lot of privacy at work place compare to todays privacy at work place, For exmple the room space elocation to an employee used to more but now a days the space av Register Now.

During the earlier days employees have lot of privacy at work place compare to todays privacy at wor.

cloud technology NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Characteristics of cloud services are elasticity and self-provisioning. These two terminologies mean that customers get access to cloud services on an on-demand basis and they are free to terminate th Register Now.

analyzing case NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 It has changed my perception as a Google employee. From the case incident I came to know Google choose the employers with smart thinking with professional competency and it is their success to lead th Register Now.

Ethics Paper NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 ETHICS PROJECT Topic The Bank Manager's Dilemma Background: Participants: Pro: Polyseenia Lewis & Victoria Con: Sai Kiran Tondapu & Jigar Gandhi Pro Highlights 1) Silver Valley is a new account for th Register Now.

ETHINICITY NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 ETHINICITY India has a vast population of more than 100 crores, it has different cultures, so many religions, variety of social habits. It has 35 states and each state has each cultural habits and it Register Now.

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This is the tendency for individuals to expand less effort when working collectively than when worki NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 This is the tendency for individuals to expand less effort when working collectively than when working individually. Describe your experience with social loafing. How have you dealt with this concept Register Now.

This is the tendency for individuals to expand less effort when working collectively than when worki.

CASINO PROJECT NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 THE CASINO 1 DATABASE ADMINISTRATION PROJECT THE CASINO TO BE FILLED IN BY THE STUDENT YEAR: SEMESTER: TEAM MEMBERS: _ _ _ - TO BE FILLED IN BY PROFESSOR: GRADING: ENTITIES: 25 POINTS RELATIONSHIPS: 25 Register Now.

DELUXE department store design project NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 DATABASE DESIGN PROJECT DELUXE DEPARTMENT STORE Please prepare Powerpoint slides in order to present your solutions to DELUXE The Deluxe Department Store wants to computerize its record keeping. You h Register Now.

DELUXE department store design project.

BUSINESS CASE vs business planing 2 NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 BUSINESS CASE: Business case can be defined as a case or a notified reason which can be proposed by the managers to the owners about it to prevent the loss in business. This can be well documented for Register Now.

BUSINESS CASE vs business planing 2.

Interview information NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 PLEASE PROVIDE THE FOLLOWING INFORMATION FOR 2014 TAX RETURN PREPARATION: Please mention if you have filed last year tax return with us, if not then please mention the referred person name for securit Register Now.

Kelly NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 The clashes in culture, customs and expectations that occurred in this situation with Kelly and her new colleagues were quite obvious. Firstly Kelly, her husband Joe and her two children were very muc Register Now.

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assignment 6 unit 9 and 10.

AstraZeneca Assignment NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Running head: EXPATRIATE MANAGEMENT 1 Expatriate Management 1 Abstract AstraZeneca is founded in 1999 through the merger of the Sweden based Astra AB and the UK - based Zeneca Group Expatriate Manageme Register Now.

Expertraite NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 An emerging market is a country that has some characteristics of a developed market, but does not meet standards to be a developed market. This includes countries that may become developed markets in Register Now.

RESTORE SCENARIOS 1 FOR CLASSROOM EXAMPLES NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 FALL 2005 FINAL EXAM 1 EXAMPLES OF RESTORE SCENARIOS FOR THE CLASSROOM 1 Using figure 1 and charts 1, 2 and 3, answer the following recovery questions. All jobs are update jobs and have 2 log tapes. T Register Now.

RESTORE SCENARIOS 1 FOR CLASSROOM EXAMPLES.

Week 7 discussion NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 What are process specifications? What are the goals of process specification? The system analysts need to comprehend the standards of association and for working information of information get-togethe Register Now.

Week 7 discussion.

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week 6 discussion.

chapter 7 hero NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 [Type text] Summary 2 Abstract System analyst have to draw the data flow diagrams with graphical representation of data process that flow with a technique and it explains about the logical data flow d Register Now.

chapter 6 hero NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 [Type text] [Type text] [Type text] [Type text] Summary 2 Abstract Prototyping is used to collect the data using the information gathering technique which has values, principles, user roles, advantage Register Now.

battleofthedollar082905 NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 US Global Markets Research Deutsche [email protected] August 29, 2005 FX Research Reconciling the Cyclical and Structural Forces on the Dollar Structural forces point to further dollar weakness, but cyclical supp Register Now.

4-Counter trend systems NYU Trading Strategy MBA INFO-GB.23 - Spring 2016 Trading Strategies and Systems: Counter-Trend Systems Vasant Dhar* Stern School of Business & NYU Center for Data Science / Editor-in-Chief, Big Data Professor vasantdhar Vasant Dhar 2016 The Questi Register Now.

4-Counter trend systems.

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Technology and Algorithmic Finance Track.

Graduates of the Technology and Algorithmic Finance Track are actively involved in the development and implementation of the entire spectrum of algorithmic trading strategies, software applications, databases and networks used in modern financial services firms. The techniques it applies bridge computer science and finance to prepare graduate to participate in large-scale and mission-critical projects. Applications include high frequency finance, behavioral finance, agent - based modeling and algorithmic trading and portfolio management.

Upon graduation, students of the Technology and Algorithmic Finance track will have developed software projects ranging from behavioral models to bespoke derivative valuations to financial trading, information management and tools and financial platforms. Students would be familiar with the use and role of technology in front, middle, and back offices; common trading strategies and how to implement and back-test them; and how to create new models and build new useful tools quickly.

Required to Complete the Financial Engineering MS program:

5 core courses, each 3 credits totaling 15 credits Track-required courses totaling 7.5 credits 1 required applied lab worth 1.5 credits 6 credits of electives 1 capstone experience of 3 credits Capstone assessment (0 credits) Bloomberg certification (0 credits)

Track-Required Course:

3 Credits Foundations of Financial Technology FRE-GY 6153 Financial Institutions spend billions per year to exploit the latest development in information technology. This course introduces a framework with which to understand and leverage information technology. The technology components covered include telecommunications, groupware, imaging and document processing, artificial intelligence, networks, protocols, risk, and object-oriented analysis and design. the course also covers the entire technological-planning process specifically for financial institutions.

3 Courses from the Following:

1.5 Credits Clearing and Settlement and Operational Risk FRE-GY 6131 This course focuses on issues involved in processing financial transactions—from order execution to final settlement of transactions—and operational risk in general. The course examines the procedures and market conventions for processing, verifying, and confirming completed transactions; resolving conflicts; decisions involved in developing clearing operations or purchasing clearing services; the role played by clearing houses; and numerous issues associated with cross-border transactions. The course also examines the effects of transaction processing, liquidity management, organizational structure, and personnel and compliance on the nature of operational risk. Qualitative and quantitative measures of operational risk are discussed.

1.5 Credits Statistical Arbitrage FRE-GY 7121 Statistical arbitrage refers to strategies that combine many relatively independent positive expected value trades so that profit, while not guaranteed, becomes very likely. This course prepares students to research and practice in this area by providing the tools and techniques to generate and evaluate individual trading strategies, combine them into a coherent portfolio, manage the resulting risks, and monitor for excess deviations from expected performance. It introduces theoretical concepts such as cointegration, risk capital allocation, proper backtesting, and factor analysis, as well as practical considerations such as data mining, automated systems, and trade execution. Programming languages such as R, Python, or C++ will be used to present applications to data at low, intermediate and high frequency.

1.5 Credits News Analytics & Strategies FRE-GY 7261 The fast-growing field of news analytics requires large databases, fast computation, and robust statistics. This course introduces the tools and techniques of analyzing news, how to quantify textual items based on, for example, positive or negative sentiment, relevance to each stock, and the amount of novelty in the content. Applications to trading strategies are discussed, including both absolute and relative return strategies, and risk management strategies. Students will be exposed to leading software in this cutting-edge space.

1.5 Credits Topics in Financial and Risk Engineering I FRE-GY 7831 Current and selected topics of particular importance in finance and risk engineering are analyzed and discussed. Selected topics are emphasized and provide a focus for further study. Topics include Credit Risk and Credit Derivatives, Quantitative Methods in Rare Events, Energy, Oil and Water Finance as well as advanced topics in financial econometrics and computational finance.

Recommended Electives (6 credits):

1.5 Credits Extreme Risk Analytics FRE-GY 6041 The course covers failures of financial theory in risk management, deriving from fundamental definitions and assumptions in modeling, including pricing formulae; convexity; stochasticity and volatility; "fat tails"; e risco. Other topics: Portfolio robustness and extreme markets and moral hazard; data-mining biases and decision error; and decision-making with incomplete information.

1.5 Credits Topics in Risk Finance I FRE-GY 7821 Current topics of particular importance in Actuarial Science are analyzed and discussed. Course topics may include for example: Pension Funds management, Actuarial Science and Social Security, Life Insurance, Insurance and Financial Products design and management.

1.5 Credits Topics in Financial and Risk Engineering 2 FRE-GY 7851 Current topics of particular importance in finance and risk engineering are analyzed and discussed. Selected topics are emphasized and provide a focus for further study. Examples can include urban finance engineering, environmental finance, infrastructure and projects finance, real-estate finance, insurance finance and derivatives, and macro hedge funds management.

Recommended Labs (1.5 credits*):

The following are recommended labs for this track:

1.5 Credits R in Finance FRE-GY 6871 This course introduces the free programming language R and its many applications to finance including risk management, portfolio construction, strategy development and testing, and trading and execution. Topics covered include financial time series analysis, advanced risk tools, applied econometrics, portfolio management, and derivatives valuation. Students will be required to write some code in R every week.

*FRE-GY 6883 counts both as a lab (1.5 credits) and as an elective (1.5 credits), totaling 3 credits.

MS in Management and Systems (STEM)

Ready to Proceed?

M. S. in Management and Systems.

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To lead successfully in the business world, you need to understand the nuances of business and how they interface with the most advanced innovations in technology. With information technology (IT) having become an indispensible element of the business world, strong managers with information and systems specialties are in high demand. These positions are now closely aligned with the C-Suite, and to excel in them, you must be as adept at crafting budgets and understanding organizational structures as you are in designing and implementing company-wide computer upgrades.

The MS in Management and Systems is a unique program that provides you with strong business leadership skills and comprehensive knowledge of information technologies that can enable an organization to improve its financial or marketplace performance. With a focus on the alignment and the integration of management information systems and technology with key business strategies, the program provides you the competitive advantage of understanding organizations as complex systems with a shared vision.

The program can be completed fully online, or in a traditional classroom setting, or by blending a combination of the two formats. The curriculum is taught by accomplished practitioners who possess significant management expertise and in-depth knowledge of the latest innovations in information technology. Students can select one of four concentrations:

Database technologies Enterprise risk management Strategy and leadership Systems management.

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