Machine Learning in Finance

Machine Learning in Finance

Author: Matthew F. Dixon

Publisher: Springer Nature

Published: 2020-07-01

Total Pages: 565

ISBN-13: 3030410684

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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.


RETRACTED BOOK: 151 Trading Strategies

RETRACTED BOOK: 151 Trading Strategies

Author: Zura Kakushadze

Publisher: Springer

Published: 2018-12-13

Total Pages: 480

ISBN-13: 3030027929

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The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.


Global Waves of Debt

Global Waves of Debt

Author: M. Ayhan Kose

Publisher: World Bank Publications

Published: 2021-03-03

Total Pages: 403

ISBN-13: 1464815453

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The global economy has experienced four waves of rapid debt accumulation over the past 50 years. The first three debt waves ended with financial crises in many emerging market and developing economies. During the current wave, which started in 2010, the increase in debt in these economies has already been larger, faster, and broader-based than in the previous three waves. Current low interest rates mitigate some of the risks associated with high debt. However, emerging market and developing economies are also confronted by weak growth prospects, mounting vulnerabilities, and elevated global risks. A menu of policy options is available to reduce the likelihood that the current debt wave will end in crisis and, if crises do take place, will alleviate their impact.


Foundations of Deep Reinforcement Learning

Foundations of Deep Reinforcement Learning

Author: Laura Graesser

Publisher: Addison-Wesley Professional

Published: 2019-11-20

Total Pages: 629

ISBN-13: 0135172489

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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.


Stochastic Finance

Stochastic Finance

Author: Hans Föllmer

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2016-07-25

Total Pages: 608

ISBN-13: 3110463458

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This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry. The focus on stochastic models in discrete time has two immediate benefits. First, the probabilistic machinery is simpler, and one can discuss right away some of the key problems in the theory of pricing and hedging of financial derivatives. Second, the paradigm of a complete financial market, where all derivatives admit a perfect hedge, becomes the exception rather than the rule. Thus, the need to confront the intrinsic risks arising from market incomleteness appears at a very early stage. The first part of the book contains a study of a simple one-period model, which also serves as a building block for later developments. Topics include the characterization of arbitrage-free markets, preferences on asset profiles, an introduction to equilibrium analysis, and monetary measures of financial risk. In the second part, the idea of dynamic hedging of contingent claims is developed in a multiperiod framework. Topics include martingale measures, pricing formulas for derivatives, American options, superhedging, and hedging strategies with minimal shortfall risk. This fourth, newly revised edition contains more than one hundred exercises. It also includes material on risk measures and the related issue of model uncertainty, in particular a chapter on dynamic risk measures and sections on robust utility maximization and on efficient hedging with convex risk measures. Contents: Part I: Mathematical finance in one period Arbitrage theory Preferences Optimality and equilibrium Monetary measures of risk Part II: Dynamic hedging Dynamic arbitrage theory American contingent claims Superhedging Efficient hedging Hedging under constraints Minimizing the hedging error Dynamic risk measures


TAIL RISK HEDGING: Creating Robust Portfolios for Volatile Markets

TAIL RISK HEDGING: Creating Robust Portfolios for Volatile Markets

Author: Vineer Bhansali

Publisher: McGraw Hill Professional

Published: 2013-12-27

Total Pages: 272

ISBN-13: 0071791760

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"TAIL RISKS" originate from the failure of mean reversion and the idealized bell curve of asset returns, which assumes that highly probable outcomes occur near the center of the curve and that unlikely occurrences, good and bad, happen rarely, if at all, at either "tail" of the curve. Ever since the global financial crisis, protecting investments against these severe tail events has become a priority for investors and money managers, but it is something Vineer Bhansali and his team at PIMCO have been doing for over a decade. In one of the first comprehensive and rigorous books ever written on tail risk hedging, he lays out a systematic approach to protecting portfolios from, and potentially benefiting from, rare yet severe market outcomes. Tail Risk Hedging is built on the author's practical experience applying macroeconomic forecasting and quantitative modeling techniques across asset markets. Using empirical data and charts, he explains the consequences of diversification failure in tail events and how to manage portfolios when this happens. He provides an easy-to-use, yet rigorous framework for protecting investment portfolios against tail risk and using tail hedging to play offense. Tail Risk Hedging explores how to: Generate profits from volatility and illiquidity during tail-risk events in equity and credit markets Buy attractively priced tail hedges that add value to a portfolio and quantify basis risk Interpret the psychology of investors in option pricing and portfolio construction Customize explicit hedges for retirement investments Hedge risk factors such as duration risk and inflation risk Managing tail risk is today's most significant development in risk management, and this thorough guide helps you access every aspect of it. With the time-tested and mathematically rigorous strategies described here, including pieces of computer code, you get access to insights to help mitigate portfolio losses in significant downturns, create explosive liquidity while unhedged participants are forced to sell, and create more aggressive yet tail-risk-focused portfolios. The book also gives you a unique, higher level view of how tail risk is related to investing in alternatives, and of derivatives such as zerocost collars and variance swaps. Volatility and tail risks are here to stay, and so should your clients' wealth when you use Tail Risk Hedging for managing portfolios. PRAISE FOR TAIL RISK HEDGING: "Managing, mitigating, and even exploiting the risk of bad times are the most important concerns in investments. Bhansali puts tail risk hedging and tail risk management under a microscope--pricing, implementation, and showing how we can fine-tune our risk exposures, which are all crucial ways in how we can better weather our bad times." -- ANDREW ANG, Ann F. Kaplan Professor of Business at Columbia University "This book is critical and accessible reading for fiduciaries, financial consultants and investors interested in both theoretical foundations and practical considerations for how to frame hedging downside risk in portfolios. It is a tremendous resource for anyone involved in asset allocation today." -- CHRISTOPHER C. GECZY, Ph.D., Academic Director, Wharton Wealth Management Initiative and Adj. Associate Professor of Finance, The Wharton School "Bhansali's book demonstrates how tail risk hedging can work, be concretely implemented, and lead to higher returns so that it is possible to have your cake and eat it too! A must read for the savvy investor." -- DIDIER SORNETTE, Professor on the Chair of Entrepreneurial Risks, ETH Zurich


Financial Signal Processing and Machine Learning

Financial Signal Processing and Machine Learning

Author: Ali N. Akansu

Publisher: John Wiley & Sons

Published: 2016-04-21

Total Pages: 312

ISBN-13: 1118745639

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The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.


Risk Analysis and Portfolio Modelling

Risk Analysis and Portfolio Modelling

Author: Elisa Luciano

Publisher: MDPI

Published: 2019-10-16

Total Pages: 224

ISBN-13: 3039216244

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Financial Risk Measurement is a challenging task, because both the types of risk and the techniques evolve very quickly. This book collects a number of novel contributions to the measurement of financial risk, which address either non-fully explored risks or risk takers, and does so in a wide variety of empirical contexts.


Computational Methods for Risk Management in Economics and Finance

Computational Methods for Risk Management in Economics and Finance

Author: Marina Resta

Publisher: MDPI

Published: 2020-04-02

Total Pages: 234

ISBN-13: 3039284983

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At present, computational methods have received considerable attention in economics and finance as an alternative to conventional analytical and numerical paradigms. This Special Issue brings together both theoretical and application-oriented contributions, with a focus on the use of computational techniques in finance and economics. Examined topics span on issues at the center of the literature debate, with an eye not only on technical and theoretical aspects but also very practical cases.


Financial Theory and Corporate Policy

Financial Theory and Corporate Policy

Author: Thomas E. Copeland

Publisher:

Published: 2013-07-17

Total Pages: 924

ISBN-13: 9781292021584

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This classic textbook in the field, now completely revised and updated, provides a bridge between theory and practice. Appropriate for the second course in Finance for MBA students and the first course in Finance for doctoral students, the text prepares students for the complex world of modern financial scholarship and practice. It presents a unified treatment of finance combining theory, empirical evidence and applications.