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Applications include high frequency finance, behavioral finance, agent- based modeling and algorithmic trading and portfolio management. 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 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. 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.
Student teams will prepare and present projects or case studies applying hte concepts covered in class. 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.
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. 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.
Selected topics are emphasized and provide a focus for further study. Portfolio robustness and extreme markets and moral hazard; data-mining biases and decision error; and decision-making with incomplete information. 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. 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. The focus is on the principles and practice of financial engineering and risk management and on developing intuition: 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. The primary prerequisite is familiarity with standard option pricing and Greeks. A portion of the final exam may involve a live computation project. 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. Selected topics are emphasized and provide focus for further study. Course topics may include for example: Examples can include urban finance engineering, environmental finance, infrastructure and projects finance, real-estate finance, insurance finance and derivatives, and macro hedge funds management. 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. Facebook Twitter Instagram YouTube. Preview The New Tandon Website! Graduate Standing 3 Courses from the Following: The following are recommended labs for this track: Financial Markets and Corporate Finance Track: Technology and Algorithmic Finance Track: