Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Alternative Data And Artificial Intelligence Techniques
Download Alternative Data And Artificial Intelligence Techniques full books in PDF, epub, and Kindle. Read online Alternative Data And Artificial Intelligence Techniques ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Alternative Data and Artificial Intelligence Techniques by : Qingquan Tony Zhang
Download or read book Alternative Data and Artificial Intelligence Techniques written by Qingquan Tony Zhang and published by Springer Nature. This book was released on 2022-10-31 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.
Book Synopsis Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by : El Bachir Boukherouaa
Download or read book Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Book Synopsis Artificial Intelligence and Credit Risk by : Rossella Locatelli
Download or read book Artificial Intelligence and Credit Risk written by Rossella Locatelli and published by Springer Nature. This book was released on 2022-09-13 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as “traditional” and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the new AI toolbox on new (unconventional) data to enhance the models’ predictive power, without neglecting problems due to results’ interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions.
Book Synopsis Fintech with Artificial Intelligence, Big Data, and Blockchain by : Paul Moon Sub Choi
Download or read book Fintech with Artificial Intelligence, Big Data, and Blockchain written by Paul Moon Sub Choi and published by Springer Nature. This book was released on 2021-03-08 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain—all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths.
Book Synopsis Handbook of Alternative Data in Finance, Volume I by : Gautam Mitra
Download or read book Handbook of Alternative Data in Finance, Volume I written by Gautam Mitra and published by CRC Press. This book was released on 2023-07-12 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike. Features Includes cutting edge applications in machine learning, fintech, and more Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics Features chapters from many leading researchers and practitioners
Book Synopsis Big Data and Machine Learning in Quantitative Investment by : Tony Guida
Download or read book Big Data and Machine Learning in Quantitative Investment written by Tony Guida and published by John Wiley & Sons. This book was released on 2019-03-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Book Synopsis The Book of Alternative Data by : Alexander Denev
Download or read book The Book of Alternative Data written by Alexander Denev and published by John Wiley & Sons. This book was released on 2020-06-29 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.
Book Synopsis Handbook of Artificial Intelligence and Big Data Applications in Investments by : Larry Cao
Download or read book Handbook of Artificial Intelligence and Big Data Applications in Investments written by Larry Cao and published by CFA Institute Research Foundation. This book was released on 2023-04-24 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) and big data have their thumbprints all over the modern asset management firm. Like detectives investigating a crime, the practitioner contributors to this book put the latest data science techniques under the microscope. And like any good detective story, much of what is unveiled is at the same time surprising and hiding in plain sight. Each chapter takes you on a well-guided tour of the development and application of specific AI and big data techniques and brings you up to the minute on how they are being used by asset managers. Given the diverse backgrounds and affiliations of our authors, this book is the perfect companion to start, refine, or plan the next phase of your data science journey.
Book Synopsis Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance by : Sachin S. Kamble
Download or read book Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance written by Sachin S. Kamble and published by Springer Nature. This book was released on 2023-02-03 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the interplay between digital transformation, industry 4.0 technologies, and sustainable supply chain performance. The book mainly focuses on presenting case studies and empirical studies demonstrating how the industry 4.0 technologies interact with the conventional manufacturing practices such as lean manufacturing, circular economy practices, total quality management, and maintenance management, while achieving enhanced sustainable supply chain performance. The book guides the practitioners to consider the status of conventional supply chains in their organisations while designing industry 4.0 systems. This book is a useful resource for researchers and academicians to understand the interplay between existing technologies, industry 4.0 technologies, and sustainable performance in the digital transformation journey.
Download or read book The AI Book written by Ivana Bartoletti and published by John Wiley & Sons. This book was released on 2020-06-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important
Book Synopsis Applications of Computational Intelligence in Concrete Technology by : Sakshi Gupta
Download or read book Applications of Computational Intelligence in Concrete Technology written by Sakshi Gupta and published by CRC Press. This book was released on 2022-06-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence (CI) in concrete technology has not yet been fully explored worldwide because of some limitations in data sets. This book discusses the selection and separation of data sets, performance evaluation parameters for different types of concrete and related materials, and sensitivity analysis related to various CI techniques. Fundamental concepts and essential analysis for CI techniques such as artificial neural network, fuzzy system, support vector machine, and how they work together for resolving real-life problems, are explained. Features: It is the first book on this fast-growing research field. It discusses the use of various computation intelligence techniques in concrete technology applications. It explains the effectiveness of the methods used and the wide range of available techniques. It integrates a wide range of disciplines from civil engineering, construction technology, and concrete technology to computation intelligence, soft computing, data science, computer science, and so on. It brings together the experiences of contributors from around the world who are doing research in this field and explores the different aspects of their research. The technical content included is beneficial for researchers as well as practicing engineers in the concrete and construction industry.
Book Synopsis Contemporary Issues in Behavioral Finance by : Simon Grima
Download or read book Contemporary Issues in Behavioral Finance written by Simon Grima and published by Emerald Group Publishing. This book was released on 2019-07-04 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This special edition of Contemporary Studies in Economic and Financial Analysis offers seventeen chapters from invited participants in the International Applied Social Science Congress, held in Turkey between the 19th and 21st April 2018.
Book Synopsis Machine Learning for Asset Management by : Emmanuel Jurczenko
Download or read book Machine Learning for Asset Management written by Emmanuel Jurczenko and published by John Wiley & Sons. This book was released on 2020-10-06 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Book Synopsis Handbook of Intelligent and Sustainable Manufacturing by : Ajay Kumar
Download or read book Handbook of Intelligent and Sustainable Manufacturing written by Ajay Kumar and published by CRC Press. This book was released on 2024-08-28 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent and sustainable manufacturing is a broad category of manufacturing that employs computer-integrated manufacturing, high levels of adaptability and rapid design changes, digital information technology, and more flexible technical workforce training. Other goals sometimes include fast changes in production levels based on demand, optimization of the production system, efficient production, and recyclability. This handbook provides compiled knowledge of intelligent and sustainable manufacturing within the context of Industry 4.0. along with tools, principles, and strategies. Handbook of Intelligent and Sustainable Manufacturing: Tools, Principles, and Strategies offers recent developments, future outlooks, and advanced and analytical modeling techniques of intelligent and sustainable manufacturing with examples backed up by experimental and numerical data. It bridges the gap between R&D in intelligent and sustainable manufacturing–related fields and presents case studies and solutions alongside social and green environmental impact. The handbook includes a wide range of advanced tools and applications with modeling results and explains how different internet technologies integrate the manufacturing approach with people, products, and complex systems. By encompassing advanced technologies such as digital twins, big data informatics, artificial intelligence, nature-inspired algorithms, IoT, Industry 4.0, simulation approaches, analytical strategies, quality tools, roots and pillars, diagnostic tools, and methodical strategies, this handbook provides the most up-to-date and advanced information source available. This handbook will help industries and organizations to implement intelligent manufacturing and move towards the sustainability of manufacturing practices. It will also serve as a reference for senior graduate-level courses in mechanical, production, industrial, and aerospace engineering and a value-added asset to libraries of all technical institutions.
Book Synopsis Artificial Intelligence in Finance by : Nydia Remolina
Download or read book Artificial Intelligence in Finance written by Nydia Remolina and published by Edward Elgar Publishing. This book was released on 2023-01-20 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive analysis of the primary challenges, opportunities and regulatory developments associated with the use of artificial intelligence (AI) in the financial sector. It will show that, while AI has the potential to promote a more inclusive and competitive financial system, the increasing use of AI may bring certain risks and regulatory challenges that need to be addressed by regulators and policymakers.
Book Synopsis Deep Learning Tools for Predicting Stock Market Movements by : Renuka Sharma
Download or read book Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-04-10 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
