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 finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

A book which reveals the people and ideas on the cusp of a new era in finance.

Using a variety of theoretical frameworks drawn from the social sciences, the contributions in this edited collection offer a critical perspective on the dominant paradigms used in contemporary financial activities. Through a detailed study of the organisation and functioning of financial intermediaries and institutions, the contributors to this volume analyse ‘finance in the making’, by shedding light on the structuring of banking and financial systems, on their capacity to prescribe action and control, on their modes of regulation and, more generally, on the process of financialisation. Contributions presented in this volume have been written by authors working within the ‘social studies of finance’ tradition, a research programme that emerged twenty years ago, with the aim of addressing a diversity of financial fieldworks and related theoretical questions. This book, therefore, sheds light on different areas that are representative of contemporary financial realities. Specifically, it first studies the work of financial employees: traders, salespeople, investment managers, financial analysts, investment consultants, etc. but also provides an analysis of a range of financial instruments: financial schemes and contracts, financial derivatives, socially responsible investment funds, as well as market rules and regulations. Finally, it puts into perspective the organisations contributing to this financial reality: those developing and selling financial services (retail banks, brokerage houses, asset management firms, private equity firms, etc.), and also those contributing to the regulation of such activities (banking regulators, financial market authorities, credit rating agencies, the State, to name a few). Each text can be read without any specific knowledge of finance; the book is thus addressed to anyone willing to better understand the intricacies of contemporary financial realities.

In 2019, MIT hosted a 75th birthday symposium in honor of Robert C. Merton. The event included presentations by students and colleagues explaining the influence Merton has had on the profession and on their ideas. Each presenter focused on a specific aspect of Merton’s life and contributions so that the audience could gain a full picture of Merton’s influence while avoiding repetition across presentations. The brief contains edited transcripts of some of the speeches and panel discussions that took place at the symposium. The presentations cover Merton’s career, highlighting both his foundational work on continuous time finance and the functional approach to understanding organizations as well as recent work on retirement security and trust. Some of the presentations unveil new aspects of his life. Merton’s father, Robert K. Merton, was one of the most important sociologists of the 20th century, being the originator of concepts such as role model, unanticipated consequences, and self-fulfilling prophecies. Another of the presentations makes a convincing case for Merton as the first financial engineer; the presenter argues that a body of knowledge becomes a science when a field of engineering emerges from it. If that is the case, this brief achieves two goals. It celebrates the influence of Merton on the theory and practice of finance through a series of engaging presentations, and it traces the birth of finance as a science on its own.

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

This book discusses many key topics in investment and risk management, the global economic situation and the shift in global investment strategies. It was largely written during the period of 2007-12, one of the most tumultuous times in global financial markets which called into question not only tenets of economic forecasting and also asset allocation and return strategies. It contains studies of how investors lose money in derivative markets, examples of those who did not and how these disasters could have been prevented. The authors draw some conclusions on the impact of the structural shifts currently underway in the global economy as well as how cyclical trends will affect these industries, the globe and key sectors. The authors zoom in on key growth areas, including emerging markets, their interlinkages and financial trends. The book also covers risk arbitrage and mean reversion strategies in financial and sports betting markets, plus incentives, volatility aspects, risk taking and investments strategies used by hedge funds and university endowments. Topics such as stock market crash predictions, asset liability planning models, various players in financial markets and the evaluation of the greatest investors are also discussed. The book presents tools and case studies of real applications for analyzing a wide variety of investment returns and better assessing the risks which many investors have preferred to ignore in the search of returns. Many security market regularities or anomalies are discussed including political party and January effects as is the process of building scenarios and using Kelly and fractional Kelly strategies to optimize returns.

Hardbound. The Handbook of Finance is a primary reference work for financial economics and financial modeling students, faculty and practitioners. The expository treatments are suitable for masters and PhD students, with discussions leading from first principles to current research, with reference to important research works in the area. The Handbook is intended to be a synopsis of the current state of various aspects of the theory of financial economics and its application to important financial problems. The coverage consists of thirty-three chapters written by leading experts in the field. The contributions are in two broad categories: capital markets and corporate finance.