Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Winner of the prestigious Paul A. Samuelson Award for scholarly writing on lifelong financial security, John Cochrane's Asset Pricing now appears in a revised edition that unifies and brings the science of asset pricing up to date for advanced students and professionals. Cochrane traces the pricing of all assets back to a single idea - price equals expected discounted payoff - that captures the macro-economic risks underlying each security's value. By using a single, stochastic discount factor rather than a separate set of tricks for each asset class, Cochrane builds a unified account of modern asset pricing. He presents applications to stocks, bonds, and options. Each model - consumption based, CAPM, multifactor, term structure, and option pricing - is derived as a different specification of the discounted factor. The discount factor framework also leads to a state-space geometry for mean-variance frontiers and asset pricing models. It puts payoffs in different states of nature on the axes rather than mean and variance of return, leading to a new and conveniently linear geometrical representation of asset pricing ideas. Cochrane approaches empirical work with the Generalized Method of Moments, which studies sample average prices and discounted payoffs to determine whether price does equal expected discounted payoff. He translates between the discount factor, GMM, and state-space language and the beta, mean-variance, and regression language common in empirical work and earlier theory. The book also includes a review of recent empirical work on return predictability, value and other puzzles in the cross section, and equity premium puzzles and their resolution. Written to be a summary for academics and professionals as well as a textbook, this book condenses and advances recent scholarship in financial economics.

March 29, 1900, is considered by many to be the day mathematical finance was born. On that day a French doctoral student, Louis Bachelier, successfully defended his thesis Thorie de la Spculation at the Sorbonne. The jury, while noting that the topic was "far away from those usually considered by our candidates," appreciated its high degree of originality. This book provides a new translation, with commentary and background, of Bachelier's seminal work. Bachelier's thesis is a remarkable document on two counts. In mathematical terms Bachelier's achievement was to introduce many of the concepts of what is now known as stochastic analysis. His purpose, however, was to give a theory for the valuation of financial options. He came up with a formula that is both correct on its own terms and surprisingly close to the Nobel Prize-winning solution to the option pricing problem by Fischer Black, Myron Scholes, and Robert Merton in 1973, the first decisive advance since 1900. Aside from providing an accurate and accessible translation, this book traces the twin-track intellectual history of stochastic analysis and financial economics, starting with Bachelier in 1900 and ending in the 1980s when the theory of option pricing was substantially complete. The story is a curious one. The economic side of Bachelier's work was ignored until its rediscovery by financial economists more than fifty years later. The results were spectacular: within twenty-five years the whole theory was worked out, and a multibillion-dollar global industry of option trading had emerged.

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

《布朗运动和随机计算(第2版)》是Springer《数学研究生丛书》之113卷，是国内外公认的金融数学经典教材，各章有习题详解。

A nonmeasure theoretic introduction to stochastic processes. Considers its diverse range of applications and provides readers with probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments; a new chapter on Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs. Numerous exercises and problems have been added throughout the text.

《随机过程》是日本著名数学家伊藤清的著作，是随机过程方面的经典名著，篇幅短小，叙述精辟，具有较高的理论水平。书中以简练的笔法介绍了随机过程论的主要方面，包括可加过程、平稳过程和Markoff过程，并概述了一维扩散过程。具有初步概率论和泛函分析知识的读者，可以借此快速掌握随机过程的基本理论。

本书全面阐述了金融时间序列，并主要介绍了金融时间序列理论和方法的当前研究热点和一些最新研究成果，尤其是风险值计算、高频数据分析、随机波动率建模和马尔科夫链蒙特卡罗方法等方面。此外，本书还系统阐述了金融计量经济模型及其在金融时间序列数据和建模中的应用，所有模型和方法的运用均采用实际金融数据，并给出了所用计算机软件的命令。较之第1版，本版主要在新的发展和实证分析方面进行了更新，新增了状态空间模型和Kalman滤波以及S-Plus命令等内容。 本书可作为时间序列分析的教材，也适用于商学、经济学、数学和统计学专业对金融的计量经济学感兴趣的高年级本科生和研究生，同时，也可作为商业、金融、保险等领域专业人士的参考书。

《金融工程中的蒙特卡罗方法(影印版)》中介绍了蒙特卡罗方法在金融中的用途，并且将模拟用作呈现金融工程中模型和思想的工具。《金融工程中的蒙特卡罗方法》大致分为三个部分。第一部分介绍了蒙特卡罗方法的基本原理，衍生定价基础以及金融工程中一些最重要模型的实现。第二部分描述了如何改进模拟精确度和效率。最后的第三部分讲述了几个特别的论题：价格敏感度估计，美式期权定价以及金融投资组合中的市场风险和信贷风险评估。

《经济决策的概率模型》是一本将概率模型用于分析风险和经济决策的入门教材。全书自始至终倾力向读者阐明，如何在复杂的现实情形中运用概率论，并将概率论晦涩的数学运算融入到生动有趣的现实经济生活中。全书的分析性工作都是在Microsoft Excel电子表格中进行的，这种方法有助于读者处理更为复杂的问题。强调电子表格建模的结果是，阅读完《经济决策的概率模型》的读者可从中学到精妙的电子表格技巧，轻松获得概率分析的应用能力。
《经济决策的概率模型》适用于经济管理类专业高年级本科生和MBA学员，也可作为从事概率论、经济决策或数量建模等课程研究的人员参考读物。