M: Finance is a market-driven corporate finance book with the latest in teaching and learning tools – all at an affordable price! With M: Finance , students receive a cost-effective, easy to read, focused text complete with study resources (both print and online) to help them review for tests and apply chapter concepts. Professors receive a text that contains all the pertinent information--yet in a more condensed format that is easier to cover. M: Finance: Meet the Future!

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

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This textbook contains the fundamentals for an undergraduate course in mathematical finance aimed primarily at students of mathematics. Assuming only a basic knowledge of probability and calculus, the material is presented in a mathematically rigorous and complete way. The book covers the time value of money, including the time structure of interest rates, bonds and stock valuation; derivative securities (futures, options), modelling in discrete time, pricing and hedging, and many other core topics. With numerous examples, problems and exercises, this book is ideally suited for independent study.

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The Revolution in Corporate Finance has established itself as a key text for students of corporate finance with wide use on a range of courses. Using seminal articles from the highly regarded Bank of America Journal of Applied Corporate Finance, it gives students real insight into the practical implications of the most recent theoretical advances in the field. This extensively revised and updated fourth edition contains a significant amount of new material while retaining key original articles from previous editions. It offers, in one volume, coverage of the latest academic thinking, written by leading financial economists in a way that is accessible to students and corporate management. Uses seminal articles from the highly regarded Bank of America Journal of Applied Corporate Finance. Gives insight into the practical implications of recent theoretical advances in the field. Enhanced by new material, including two new sections on International Finance and International Corporate Governance. Highlights contributions of Nobel Laureate Merton Miller to the field of Finance.