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Deep Learning in Quantitative Finance
The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts.Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance.Inside, you’ll find over ten chapters which apply deep learning to multiple use cases across quantitative finance.You’ll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text.Readers will be able to work through these examples directly.This book is a complete resource on how deep learning is used in quantitative finance applications.It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics.You’ll also learn about the most important software frameworks.The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging.The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniquesOffers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learningDemonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion websiteIntroduces the most important software frameworks for applying deep learning within finance This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.
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Machine Learning and AI in Finance
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events.During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems.Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables.The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions.This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features.Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
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Machine Learning for Factor Investing : Python Version
Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading.ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection.The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach.Machine learning for factor investing: Python version bridges this gap.It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability.Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors.The material is available online so that readers can reproduce and enhance the examples at their convenience.If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.
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Machine Learning for Factor Investing: R Version
Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading.ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection.The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach.Machine Learning for Factor Investing: R Version bridges this gap.It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability.Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models.All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors.The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience.If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.
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Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing
Augment your asset allocation strategy with machine learning and factor investing for unprecedented returns and growthWhether you’re managing institutional portfolios or private wealth, Quantitative Asset Management will open your eyes to a new, more successful way of investing—one that harnesses the power of big data and artificial intelligence. This innovative guide walks you through everything you need to know to fully leverage these revolutionary tools.Written from the perspective of a seasoned financial investor making use of technology, it details proven investing methods, striking a rare balance between providing important technical information without burdening you with overly complex investing theory.Quantitative Asset Management is organized into four thematic sections:Part I reveals invaluable lessons for planning and governance of investment decision-making. Part 2 discusses quantitative financial modeling, covering important topics like overfitting, mitigating unrealistic assumptions, managing substitutions, enhancing minority classes, and missing data imputation. Part 3 shows how to develop a strategy into an investment product, including the alpha models, risk models, implementation, backtesting, and cost optimization. Part 4 explains how to measure performance, learn from mistakes, manage risk, and survive financial tragedies. With Quantitative Asset Management, you have everything you need to build your awareness of other markets, ask the right questions and answer them effectively, and drive steady profits even through times of great uncertainty.
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Probabilistic Machine Learning for Finance and Investing : A Primer to the Next Generation of AI with Python
Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated.Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. These systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates.This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.These systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. Probabilistic ML is the next generation ML framework and technology for AI-powered financial and investing systems for many reasons.By moving away from flawed statistical methodologies (and a restrictive conventional view of probability as a limiting frequency), you'll move toward an intuitive view of probability as a mathematically rigorous statistical framework that quantifies uncertainty holistically and successfully.This book shows you how.
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Reinforcement Learning for Finance : A Python-Based Introduction
Reinforcement learning (RL) has led to several breakthroughs in AI.The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level.More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of reinforcement learning methods in finance. Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion.ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems. This book covers:Reinforcement learningDeep Q-learningPython implementations of these algorithmsHow to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocationThis book is the ideal reference on this topic.You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance.
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Reinforcement Learning for Finance: A Python-Based Introduction
Reinforcement Learning for Finance: A Python-Based Introduction
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