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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.
Price: 68.99 £ | Shipping*: 0.00 £
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Why have the bonds in my portfolio, which are securities, lost the most value, even though they are EU government bonds considered safe investment havens?
The value of bonds in your portfolio may have decreased due to changes in interest rates. When interest rates rise, the value of existing bonds decreases because they are paying lower interest rates than newly issued bonds. This is known as interest rate risk. Even though EU government bonds are considered safe investments, they are still subject to fluctuations in interest rates, which can impact their value. Additionally, other factors such as economic conditions, inflation expectations, and market sentiment can also affect the value of bonds in your portfolio.
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How does investing in bonds differ from investing in a bank account?
Investing in bonds involves purchasing debt securities issued by governments or corporations, which pay a fixed interest rate over a specified period of time. In contrast, investing in a bank account typically involves depositing money into a savings or checking account, where it earns a variable interest rate set by the bank. Bonds generally offer higher potential returns than bank accounts, but they also carry a higher level of risk. Additionally, bonds have a maturity date, while bank accounts provide more immediate access to funds.
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Why have the bonds in my portfolio, which are securities, lost the most value, even though they are EU government bonds considered as safe investment havens?
The value of EU government bonds in your portfolio may have decreased due to a variety of factors such as changes in interest rates, inflation expectations, or market sentiment. Even though EU government bonds are generally considered safe investment havens, they are still subject to market fluctuations and can lose value in certain economic conditions. Additionally, global events, economic uncertainty, or changes in government policies can also impact the value of these securities. It's important to monitor the market and economic conditions to understand the reasons behind the decrease in value of your bond holdings.
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Is it worth investing in Ukraine's war bonds?
Investing in Ukraine's war bonds can be a way to show support for the country during its conflict with Russia, but it also comes with risks. The situation in Ukraine is volatile and the outcome of the conflict is uncertain, which could affect the value of the bonds. Additionally, there may be concerns about the stability of the Ukrainian economy and the government's ability to repay the bonds. Therefore, investing in Ukraine's war bonds should be carefully considered and individuals should weigh the potential risks and rewards before making a decision.
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Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing
Whether you are managing institutional portfolios or private wealth, augment your asset allocation strategy with machine learning and factor investing for unprecedented returns and growthIn a straightforward and unambiguous fashion, Quantitative Asset Management shows how to take join factor investing and data science—machine learning and applied to big data.Using instructive anecdotes and practical examples, including quiz questions and a companion website with working code, this groundbreaking guide provides a toolkit to apply these modern tools to investing and includes such real-world details as currency controls, market impact, and taxes.It walks readers through the entire investing process, from designing goals to planning, research, implementation, and testing, and risk management.Inside, you’ll find:Cutting edge methods married to the actual strategies used by the most sophisticated institutionsReal-world investment processes as employed by the largest investment companiesA toolkit for investing as a professionalClear explanations of how to use modern quantitative methods to analyze investing optionsAn accompanying online site with coding and appsWritten by a seasoned financial investor who uses technology as a tool—as opposed to a technologist who invests—Quantitative Asset Management explains the author’s methods without oversimplification or confounding theory and math.Quantitative Asset Management demonstrates how leading institutions use Python and MATLAB to build alpha and risk engines, including optimal multi-factor models, contextual nonlinear models, multi-period portfolio implementation, and much more to manage multibillion-dollar portfolios. Big data combined with machine learning provide amazing opportunities for institutional investors.This unmatched resource will get you up and running with a powerful new asset allocation strategy that benefits your clients, your organization, and your career.
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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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Investing in Bonds For Dummies
Improve the strength of your portfolio with this straightforward guide to bond investing Investing in Bonds For Dummies introduces you to the basics you need to know to get started with bond investing.You’ll find details on understanding bond returns and risks, and recognizing the major factors that influence bond performance.Unlike some investing vehicles, bonds typically pay interest on a regular schedule, so you can use them to provide an income stream while you protect your capital.This easy-to-understand guide will show you how to incorporate bonds into a diversified portfolio and a solid retirement plan.Learn the ins and outs of buying and selling bonds and bond fundsUnderstand the risks and potential rewards in corporate bonds, government bonds, and beyondDiversify your portfolio by using bonds to balance stocks and other investmentsGain the fundamental information you need to make smart bond investment choicesThis Dummies investing guide is great for investors looking for a resource to help them understand, evaluate, and incorporate bonds into their current investment portfolios.
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Deep Learning for Finance : Creating Machine & Deep Learning Models for Trading in Python
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 modelsExplore the details behind reinforcement learning and see how it's used in time seriesUnderstand how to interpret performance evaluation metricsExamine technical analysis and learn how it works in financial marketsCreate technical indicators in Python and combine them with ML models for optimizationEvaluate the models' profitability and predictability to understand their limitations and potential
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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Is distance learning as recognized as traditional learning?
Distance learning is becoming increasingly recognized and accepted as a legitimate form of education, especially with the advancements in technology and the widespread availability of online courses. However, traditional learning still holds a higher level of recognition and credibility in many circles, such as certain industries or academic institutions. Ultimately, the recognition of distance learning versus traditional learning can vary depending on the context and the perceptions of individuals or organizations.
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Is distance learning worse than on-campus learning?
The effectiveness of distance learning versus on-campus learning depends on individual preferences, learning styles, and the specific course or program. Distance learning can offer flexibility and convenience for those with busy schedules or other commitments, while on-campus learning may provide more opportunities for in-person interaction and hands-on experiences. Both modalities have their own set of advantages and disadvantages, and the quality of the learning experience ultimately depends on the resources, support, and engagement available to the student in either setting.
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Can digital learning videos replace textbooks and traditional learning?
While digital learning videos can be a valuable supplement to traditional learning methods, they may not be able to fully replace textbooks and traditional learning. Textbooks provide a comprehensive and structured approach to learning, while digital videos may lack depth and detail. Additionally, some students may struggle with self-regulation and focus when using digital resources. A combination of both digital learning videos and traditional learning methods may be the most effective approach to cater to different learning styles and preferences.
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