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Summary of Irene Aldridge & Marco Avellaneda's Big Data Science in Finance
Please note: This is a companion version & not the original book. Sample Book Insights: #1 Radio-Frequency Identification, or RFID, chips are embedded in almost every product you can buy, and are used to collect data about your shopping preferences, habits, and lifestyle. #2 Big Data is not just affecting financial organizations, but all corporations. The amount of data generated by financial institutions is at a record-setting number, and yet few portfolio managers have the skills to process it. #3 Big Data Finance is not just opening doors to a select group of data scientists, but also an entire industry that is developing new approaches to harness these data sets and incorporate them into mainstream investment management. #4 The demand for Big Data scientists is growing, as companies realize the importance of efficient Big Data operations. According to Business Insider, US bank J. P. Morgan has spent nearly $10 billion dollars just in 2016 on new initiatives that include Big Data science.
Big Data Science in Finance
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative...
Quantitative Analysis In Financial Markets: Collected Papers Of The New York University Mathematical Finance Seminar (Vol Iii)
This invaluable book contains lectures presented at the Courant Institute's Mathematical Finance Seminar. The audience consisted of academics from New York University and other universities, as well as practitioners from investment banks, hedge funds and asset-management firms.
Statistical Arbitrage in the U.S. Equities Market
We study model-driven statistical arbitrage strategies in U.S. equities. Trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic components of stock returns, and model them as a mean-reverting process, which leads naturally to "contrarian'' trading signals. The main contribution of the paper is the back-testing and comparison of market-neutral PCA- and ETF- based strategies over the broad universe of U.S. equities. Back-testing shows that, after accounting for transaction costs, PCA-based strategies have an average annual Sharpe ratio of 1.44 over the period 1997 to 2007, with a much stron...
Quantitative Modeling of Derivative Securities
Quantitative Modeling of Derivative Securities demonstrates how to take the basic ideas of arbitrage theory and apply them - in a very concrete way - to the design and analysis of financial products. Based primarily (but not exclusively) on the analysis of derivatives, the book emphasizes relative-value and hedging ideas applied to different financial instruments. Using a ""financial engineering approach,"" the theory is developed progressively, focusing on specific aspects of pricing and hedging and with problems that the technical analyst or trader has to consider in practice. More than just an introductory text, the reader who has mastered the contents of this one book will have breached the gap separating the novice from the technical and research literature.
Quantitative Analysis In Financial Markets: Collected Papers Of The New York University Mathematical Finance Seminar (Vol Ii)
This book contains lectures delivered at the celebrated Seminar in Mathematical Finance at the Courant Institute. The lecturers and presenters of papers are prominent researchers and practitioners in the field of quantitative financial modeling. Most are faculty members at leading universities or Wall Street practitioners.The lectures deal with the emerging science of pricing and hedging derivative securities and, more generally, managing financial risk. Specific articles concern topics such as option theory, dynamic hedging, interest-rate modeling, portfolio theory, price forecasting using statistical methods, etc.
Quantitative Analysis in Financial Markets
This volume contains lectures delivered at the Seminar in Mathematical Finance at the Courant Institute, New York University. Subjects covered include: the emerging science of pricing and hedging derivative securities, managing financial risk, and price forecasting using statistics.
Introduction to Mathematical Finance
The foundation for the subject of mathematical finance was laid nearly 100 years ago by Bachelier in his fundamental work, Theorie de la speculation. In this work, he provided the first treatment of Brownian motion. Since then, the research of Markowitz, and then of Black, Merton, Scholes, and Samuelson brought remarkable and important strides in the field. A few years later, Harrison and Kreps demonstrated the fundamental role of martingales and stochastic analysis in constructing and understanding models for financial markets. The connection opened the door for a flood of mathematical developments and growth. Concurrently with these mathematical advances, markets have grown, and developmen...
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