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Deep Finance

Author : Glenn Hopper

Publisher : Leaders Press

Page : 192 pages

File Size : 31,42 MB

Release : 2021-11-16

Category : Business & Economics

ISBN : 9781637350270

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Book Description

Deep Finance is informative, enlightening, and embraces the innovation all around us - perfect for trailblazing CFOs ready to dive deep into an era of information, analytics, and Big Data. ARE YOU READY FOR A DIGITAL TRANSFORMATION? LEAD THE AGE OF ANALYTICS WITH DEEP FINANCE. Glenn Hopper uses a unique blend of financial leadership and technical expertise to help businesses of all sizes optimize and modernize. Not a software engineer? Neither is Glenn Hopper, but his story shows how any finance leader can embrace the tech innovations shaping our world to revolutionize finance operations. Accounting has come a long way since the time of the abacus, computer punch cards, or even the paper ledger. Modern finance leaders have the ability and tools to build a team that harnesses the power of business intelligence to make their jobs easier. Leaders who aren’t aware of these opportunities are simply going to be outpaced by competitors willing to adapt to the 21st century and beyond. Deep Finance will take you from asking “What Is AI?” to walking a clear path toward your own digital transformation. Elevate your leadership and be a champion for data science in your department. In Deep Finance, you will: · Study the history of accounting—and why the age of analytics is the next logical step for all finance departments. · Step into the age of artificial intelligence and view the pathway to a digital transformation. · Expand your role as CFO by integrating business intelligence and analytics into your everyday tasks. · Weigh the pros and cons of buying or building software to manage transactions, analyze and collect data, and identify trends. · Become a “New Age CFO” who can make better financial decisions and identify where your company is moving. · Develop the language to elevate your entire management team as you enter the age of artificial intelligence. Don’t get left behind. Your competitors or team members recognize the possibilities that are available to finance departments everywhere. Take the first steps toward a digital transformation and evolution to a data-driven culture. Grab your copy of Deep Finance today!



Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat

Publisher : "O'Reilly Media, Inc."

Page : 432 pages

File Size : 31,10 MB

Release : 2020-10-01

Category : Computers

ISBN : 1492073008

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Book Description

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



Deep Learning for Finance

Author : Sofien Kaabar

Publisher : "O'Reilly Media, Inc."

Page : 369 pages

File Size : 32,47 MB

Release : 2024-01-08

Category : Computers

ISBN : 1098148355

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Book Description

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential



Artificial Intelligence in Finance

Author : Yves Hilpisch

Publisher : "O'Reilly Media, Inc."

Page : 478 pages

File Size : 47,73 MB

Release : 2020-10-14

Category : Business & Economics

ISBN : 1492055387

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Book Description

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about



Machine Learning in Finance

Author : Matthew F. Dixon

Publisher : Springer Nature

Page : 565 pages

File Size : 26,37 MB

Release : 2020-07-01

Category : Business & Economics

ISBN : 3030410684

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Book Description

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.



Deep Value

Author : Tobias E. Carlisle

Publisher : John Wiley & Sons

Page : 245 pages

File Size : 13,62 MB

Release : 2014-08-18

Category : Business & Economics

ISBN : 1118747968

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Book Description

The economic climate is ripe for another golden age of shareholder activism Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations is a must-read exploration of deep value investment strategy, describing the evolution of the theories of valuation and shareholder activism from Graham to Icahn and beyond. The book combines engaging anecdotes with industry research to illustrate the principles and methods of this complex strategy, and explains the reasoning behind seemingly incomprehensible activist maneuvers. Written by an active value investor, Deep Value provides an insider's perspective on shareholder activist strategies in a format accessible to both professional investors and laypeople. The Deep Value investment philosophy as described by Graham initially identified targets by their discount to liquidation value. This approach was extremely effective, but those opportunities are few and far between in the modern market, forcing activists to adapt. Current activists assess value from a much broader palate, and exploit a much wider range of tools to achieve their goals. Deep Value enumerates and expands upon the resources and strategies available to value investors today, and describes how the economic climate is allowing value investing to re-emerge. Topics include: Target identification, and determining the most advantageous ends Strategies and tactics of effective activism Unseating management and fomenting change Eyeing conditions for the next M&A boom Activist hedge funds have been quiet since the early 2000s, but economic conditions, shareholder sentiment, and available opportunities are creating a fertile environment for another golden age of activism. Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations provides the in-depth information investors need to get up to speed before getting left behind.



A Free Nation Deep in Debt

Author : James MacDonald

Publisher : Princeton University Press

Page : 580 pages

File Size : 15,82 MB

Release : 2006-05-22

Category : Business & Economics

ISBN : 9780691126326

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Book Description

For the greater part of recorded history the most successful and powerful states were autocracies; yet now the world is increasingly dominated by democracies. In A Free Nation Deep in Debt, James Macdonald provides a novel answer for how and why this political transformation occurred. The pressures of war finance led ancient states to store up treasure; and treasure accumulation invariably favored autocratic states. But when the art of public borrowing was developed by the city-states of medieval Italy as a democratic alternative to the treasure chest, the balance of power tipped. From that point on, the pressures of war favored states with the greatest public creditworthiness; and the most creditworthy states were invariably those in which the people who provided the money also controlled the government. Democracy had found a secret weapon and the era of the citizen creditor was born. Macdonald unfolds this tale in a sweeping history that starts in biblical times, passes via medieval Italy to the wars and revolutions of the seventeenth and eighteenth centuries, and ends with the great bond drives that financed the two world wars.



The End of Finance

Author : Massimo Amato

Publisher : John Wiley & Sons

Page : 442 pages

File Size : 16,31 MB

Release : 2013-12-19

Category : Business & Economics

ISBN : 0745683657

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Book Description

This new book by two distinguished Italian economists is a highly original contribution to our understanding of the origins and aftermath of the financial crisis. The authors show that the recent financial crisis cannot be understood simply as a malfunctioning in the subprime mortgage market: rather, it is rooted in a much more fundamental transformation, taking place over an extended time period, in the very nature of finance. The ‘end’ or purpose of finance is to be found in the social institutions by which the making and acceptance of promises of payment are made possible - that is, the creation and cancellation of debt contracts within a specified time frame. Amato and Fantacci argue that developments in the modern financial system by which debts are securitized has endangered this fundamental credit/debt structure. The illusion has been created that debts are universally liquid in the sense that they need not be redeemed but can be continually sold on in increasingly extensive global markets. What appears to have reduced the riskiness of default for individual agents has in fact increased the fragility of the system as a whole. The authors trace the origins of this profound transformation backwards in time, not just to the neoliberal reforms of the 1980s and 90s but to the birth of capitalist finance in the mercantile networks of the sixteenth and seventeenth centuries. This long historical perspective and deep analysis of the nature of finance enables the authors to tackle the challenges we face today in a fresh way - not simply by tinkering with existing mechanisms, but rather by asking the more profound question of how institutions might be devised in which finance could fulfil its essential functions.



Modern Computational Finance

Author : Antoine Savine

Publisher : John Wiley & Sons

Page : 592 pages

File Size : 15,30 MB

Release : 2018-11-20

Category : Mathematics

ISBN : 1119539455

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Book Description

Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book.



Big Data Science in Finance

Author : Irene Aldridge

Publisher : John Wiley & Sons

Page : 336 pages

File Size : 30,67 MB

Release : 2021-01-08

Category : Computers

ISBN : 1119602971

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Book Description

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.