Deep Finance

Deep Finance

Author: Glenn Hopper

Publisher: Leaders Press

Published: 2021-11-16

Total Pages: 192

ISBN-13: 9781637350270

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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!


Book Synopsis Deep Finance by : Glenn Hopper

Download or read book Deep Finance written by Glenn Hopper and published by Leaders Press. This book was released on 2021-11-16 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!

Deep Finance

Deep Finance

Author: Glenn Hopper

Publisher:

Published: 2021-08-24

Total Pages: 0

ISBN-13: 9781637351246

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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!


Book Synopsis Deep Finance by : Glenn Hopper

Download or read book Deep Finance written by Glenn Hopper and published by . This book was released on 2021-08-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!

Deep Learning for Finance

Deep Learning for Finance

Author: Sofien Kaabar

Publisher: "O'Reilly Media, Inc."

Published: 2024-01-08

Total Pages: 369

ISBN-13: 1098148355

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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


Book Synopsis Deep Learning for Finance by : Sofien Kaabar

Download or read book Deep Learning for Finance written by Sofien Kaabar and published by "O'Reilly Media, Inc.". This book was released on 2024-01-08 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

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.


Book Synopsis Machine Learning in Finance by : Matthew F. Dixon

Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning and Data Science Blueprints for Finance

Machine Learning and Data Science Blueprints for Finance

Author: Hariom Tatsat

Publisher: "O'Reilly Media, Inc."

Published: 2020-10-01

Total Pages: 432

ISBN-13: 1492073008

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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


Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

A Free Nation Deep in Debt

A Free Nation Deep in Debt

Author: James MacDonald

Publisher: Princeton University Press

Published: 2006-05-22

Total Pages: 580

ISBN-13: 9780691126326

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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.


Book Synopsis A Free Nation Deep in Debt by : James MacDonald

Download or read book A Free Nation Deep in Debt written by James MacDonald and published by Princeton University Press. This book was released on 2006-05-22 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Value

Deep Value

Author: Tobias E. Carlisle

Publisher: John Wiley & Sons

Published: 2014-08-18

Total Pages: 245

ISBN-13: 1118747968

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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.


Book Synopsis Deep Value by : Tobias E. Carlisle

Download or read book Deep Value written by Tobias E. Carlisle and published by John Wiley & Sons. This book was released on 2014-08-18 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python for Finance Cookbook

Python for Finance Cookbook

Author: Eryk Lewinson

Publisher: Packt Publishing Ltd

Published: 2020-01-31

Total Pages: 426

ISBN-13: 1789617324

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Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.


Book Synopsis Python for Finance Cookbook by : Eryk Lewinson

Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

The Economist

The Economist

Author:

Publisher:

Published: 1895

Total Pages: 1032

ISBN-13:

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Book Synopsis The Economist by :

Download or read book The Economist written by and published by . This book was released on 1895 with total page 1032 pages. Available in PDF, EPUB and Kindle. Book excerpt:

DeFi and the Future of Finance

DeFi and the Future of Finance

Author: Campbell R. Harvey

Publisher: John Wiley & Sons

Published: 2021-08-24

Total Pages: 217

ISBN-13: 1119836018

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During the Global Financial Crisis in 2008, our financial infrastructure failed. Governments bailed out the very institutions that let the economy down. This episode spurred a serious rethink of our financial system. Does it make any sense that it takes two days to settle a stock transaction? Why do retailers, operating on razor thin margins, have to pay 3% for every customer credit card swipe? Why does it take two days to transfer money from a bank account to a brokerage—or any other company? Why are savings rates miniscule or negative? Why is it so difficult for entrepreneurs to get financing at traditional banks? In DeFi and the Future of Finance, Campbell R. Harvey, Ashwin Ramachandran and Joey Santoro, introduce the new world of Decentralized Finance. The book argues that the current financial landscape is ripe for disruption and we are seeing, in real time, the reinvention of finance. The authors provide the reader with a clear assessment of the problems with the current financial system and how DeFi solves many of these problems. The essence of DeFi is that we interact with peers—there is no brick and mortar and all of the associated costs. Savings and lending are reinvented. Trading takes place with algorithms far removed from traditional brokerages. The book conducts a deep dive on some of the most innovative protocols such as Uniswap and Compound. Many of the companies featured in the book you might not have heard of—however, you will in the future. As with any new technology, there are a myriad of risks and the authors carefully catalogue these risks and assess which ones can be successfully mitigated. Ideally suited for people working in any part of the finance industry as well as financial policy makers, DeFi and the Future of Finance gives readers a vision of the future. The world of finance will fundamentally be changed over the coming decade. The book enables you to become part of the disruption – not the target of the disruption.


Book Synopsis DeFi and the Future of Finance by : Campbell R. Harvey

Download or read book DeFi and the Future of Finance written by Campbell R. Harvey and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the Global Financial Crisis in 2008, our financial infrastructure failed. Governments bailed out the very institutions that let the economy down. This episode spurred a serious rethink of our financial system. Does it make any sense that it takes two days to settle a stock transaction? Why do retailers, operating on razor thin margins, have to pay 3% for every customer credit card swipe? Why does it take two days to transfer money from a bank account to a brokerage—or any other company? Why are savings rates miniscule or negative? Why is it so difficult for entrepreneurs to get financing at traditional banks? In DeFi and the Future of Finance, Campbell R. Harvey, Ashwin Ramachandran and Joey Santoro, introduce the new world of Decentralized Finance. The book argues that the current financial landscape is ripe for disruption and we are seeing, in real time, the reinvention of finance. The authors provide the reader with a clear assessment of the problems with the current financial system and how DeFi solves many of these problems. The essence of DeFi is that we interact with peers—there is no brick and mortar and all of the associated costs. Savings and lending are reinvented. Trading takes place with algorithms far removed from traditional brokerages. The book conducts a deep dive on some of the most innovative protocols such as Uniswap and Compound. Many of the companies featured in the book you might not have heard of—however, you will in the future. As with any new technology, there are a myriad of risks and the authors carefully catalogue these risks and assess which ones can be successfully mitigated. Ideally suited for people working in any part of the finance industry as well as financial policy makers, DeFi and the Future of Finance gives readers a vision of the future. The world of finance will fundamentally be changed over the coming decade. The book enables you to become part of the disruption – not the target of the disruption.