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Author: Xavier Vives
Publisher: Princeton University Press
ISBN: 140082950X
Category : Business & Economics
Languages : en
Pages : 422
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Book Description
The ways financial analysts, traders, and other specialists use information and learn from each other are of fundamental importance to understanding how markets work and prices are set. This graduate-level textbook analyzes how markets aggregate information and examines the impacts of specific market arrangements--or microstructure--on the aggregation process and overall performance of financial markets. Xavier Vives bridges the gap between the two primary views of markets--informational efficiency and herding--and uses a coherent game-theoretic framework to bring together the latest results from the rational expectations and herding literatures. Vives emphasizes the consequences of market interaction and social learning for informational and economic efficiency. He looks closely at information aggregation mechanisms, progressing from simple to complex environments: from static to dynamic models; from competitive to strategic agents; and from simple market strategies such as noncontingent orders or quantities to complex ones like price contingent orders or demand schedules. Vives finds that contending theories like informational efficiency and herding build on the same principles of Bayesian decision making and that "irrational" agents are not needed to explain herding behavior, booms, and crashes. As this book shows, the microstructure of a market is the crucial factor in the informational efficiency of prices. Provides the most complete analysis of the ways markets aggregate information Bridges the gap between the rational expectations and herding literatures Includes exercises with solutions Serves both as a graduate textbook and a resource for researchers, including financial analysts
Author: Xavier Vives
Publisher: Princeton University Press
ISBN: 140082950X
Category : Business & Economics
Languages : en
Pages : 422
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Book Description
The ways financial analysts, traders, and other specialists use information and learn from each other are of fundamental importance to understanding how markets work and prices are set. This graduate-level textbook analyzes how markets aggregate information and examines the impacts of specific market arrangements--or microstructure--on the aggregation process and overall performance of financial markets. Xavier Vives bridges the gap between the two primary views of markets--informational efficiency and herding--and uses a coherent game-theoretic framework to bring together the latest results from the rational expectations and herding literatures. Vives emphasizes the consequences of market interaction and social learning for informational and economic efficiency. He looks closely at information aggregation mechanisms, progressing from simple to complex environments: from static to dynamic models; from competitive to strategic agents; and from simple market strategies such as noncontingent orders or quantities to complex ones like price contingent orders or demand schedules. Vives finds that contending theories like informational efficiency and herding build on the same principles of Bayesian decision making and that "irrational" agents are not needed to explain herding behavior, booms, and crashes. As this book shows, the microstructure of a market is the crucial factor in the informational efficiency of prices. Provides the most complete analysis of the ways markets aggregate information Bridges the gap between the rational expectations and herding literatures Includes exercises with solutions Serves both as a graduate textbook and a resource for researchers, including financial analysts
Author: Naomi R. Lamoreaux
Publisher: University of Chicago Press
ISBN: 0226468437
Category : Business & Economics
Languages : en
Pages : 356
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Book Description
Learning by Doing in Markets, Firms, and Countries draws out the underlying economics in business history by focusing on learning processes and the development of competitively valuable asymmetries. The essays show that organizations, like people, learn that this process can be organized more or less effectively, which can have major implications for how competition works. The first three essays in this volume explore techniques firms have used to both manage information to create valuable asymmetries and to otherwise suppress unwelcome competition. The next three focus on the ways in which firms have built special capabilities over time, capabilities that have been both sources of competitive advantage and resistance to new opportunities. The last two extend the notion of learning from the level of firms to that of nations. The collection as a whole builds on the previous two volumes to make the connection between information structure and product market outcomes in business history.
Author: Deniz Ozenbas
Publisher: Springer Nature
ISBN: 3030748170
Category : Business enterprises
Languages : en
Pages : 111
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Book Description
This open access book addresses four standard business school subjects: microeconomics, macroeconomics, finance and information systems as they relate to trading, liquidity, and market structure. It provides a detailed examination of the impact of trading costs and other impediments of trading that the authors call rictions It also presents an interactive simulation model of equity market trading, TraderEx, that enables students to implement trading decisions in different market scenarios and structures. Addressing these topics shines a bright light on how a real-world financial market operates, and the simulation provides students with an experiential learning opportunity that is informative and fun. Each of the chapters is designed so that it can be used as a stand-alone module in an existing economics, finance, or information science course. Instructor resources such as discussion questions, Powerpoint slides and TraderEx exercises are available online.
Author: Marc Alvarez
Publisher: Elsevier
ISBN: 0080465781
Category : Business & Economics
Languages : en
Pages : 135
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Book Description
Market Data Explained is intended to provide a guide to the universe of data content produced by the global capital markets on a daily basis. Commonly referred to as "market data, the universe of content is very wide and the type of information correspondingly diverse. Jargon and acronyms are very common. As a result, users of marker data typically face difficulty in applying the content in analysis and business applications. This guide provides an independent framework for understanding this diversity and streamlining the process of referring to content and how it relates to today's business environment. The book achieves this goal by providing a consistent frame of reference for users of market data. As such, it is built around the concept of a data model – a single, coherent view of the capital markets independent of any one source, such as an exchange. In particular it delineates clearly between the actual data content and how it is delivered (i.e., realtime data streams versus reference data). It shows how the data relates across the universe of securities (i.e., stocks, bonds, derivatives etc.). In this way it provides a logical framework for understanding how new content can be added over time as the business develops. Special features: 1. Uniqueness – this is the first comprehensive catalog and taxonomy to be made available for a business audience 2. Industry Acceptance – the framework described in this book is implemented as a relational data model in the industry today and used by blue chip multinational firms 3. Comprehensiveness – there are no arbitrary distinctions made based on asset class or data type (the legacy approach). The model presented in this book is fully cross asset and makes no distinction between data types (i.e., realtime versus historical/reference data) or sources 4. Independence – the framework is an independent, objective overview of how the data content integrates to provide a coherent view of the data produced by the global capital markets on a daily and intra-day basis. It provides a logical framework for referring to the content and entities that are so intrinsic to this industry - First and only single, comprehensive desk reference to market data produced by the global capital markets on a daily basis - Provides a comprehensive catalog of the market data and a common structure for navigating the complex content and interrelationships - Provides a common taxonomy and naming conventions that handles the highly varied, geographically and language dependent nature of the content
Author: Christian L. Dunis
Publisher: Springer
ISBN: 1137488808
Category : Business & Economics
Languages : en
Pages : 349
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Book Description
As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.
Author: James F. Dalton
Publisher: Wiley
ISBN: 9781118531730
Category : Business & Economics
Languages : en
Pages : 368
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Book Description
A timely update to the book on using the Market Profile method to trade Emerging over twenty years ago, Market Profile analysis continues to realize a strong following among active traders. The approach explains the underlying dynamics and structure of markets, identifies value areas, price rejection points, and measures the strength of buyers and sellers. Unlike more conventional forms of technical analysis, Market Profile is an all-encompassing approach, and Mind Over Markets, Updated Edition provides traders with a solid understanding of it. Since the first edition of Mind Over Markets—considered the best book on applying Market Profile analysis to trading—was published over a decade ago, much has changed in the worlds of finance and investing. That's why James Dalton, a pioneer in the popularization of Market Profile, has returned with a new edition of this essential guide. Written to reflect today's dynamic market conditions, Mind Over Markets, Updated Edition clearly puts this unique method of interpreting market behavior and identifying trading/investment opportunities in perspective. Includes new chapters on Market Profile-based trading strategies, using Market Profile in connection with other market indicators, and much more Explains how the Market Profile approach has evolved over the past twenty-five years and how it is used by contemporary traders Written by a leading educator and authority on the Market Profile One of the key elements that has long separated successful traders from the rest is their intuitive understanding that time regulates all financial opportunities. The ability to record price information according to time has unleashed huge amounts of useful market information. Mind Over Markets, Updated Edition will show you how to profitably put this information to work for you.
Author: Marc Levinson
Publisher: The Economist
ISBN: 1541742516
Category : Business & Economics
Languages : en
Pages : 250
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Book Description
The revised and updated 7th edition of this highly regarded book brings the reader right up to speed with the latest financial market developments, and provides a clear and incisive guide to a complex world that even those who work in it often find hard to understand. In chapters on the markets that deal with money, foreign exchange, equities, bonds, commodities, financial futures, options and other derivatives, the book examines why these markets exist, how they work, and who trades in them, and gives a run-down of the factors that affect prices and rates. Business history is littered with disasters that occurred because people involved their firms with financial instruments they didn't properly understand. If they had had this book they might have avoided their mistakes. For anyone wishing to understand financial markets, there is no better guide.
Author: Anatoly B. Schmidt
Publisher: John Wiley & Sons
ISBN: 1118093658
Category : Business & Economics
Languages : en
Pages : 195
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Book Description
An informative guide to market microstructure and trading strategies Over the last decade, the financial landscape has undergone a significant transformation, shaped by the forces of technology, globalization, and market innovations to name a few. In order to operate effectively in today's markets, you need more than just the motivation to succeed, you need a firm understanding of how modern financial markets work and what professional trading is really about. Dr. Anatoly Schmidt, who has worked in the financial industry since 1997, and teaches in the Financial Engineering program of Stevens Institute of Technology, puts these topics in perspective with his new book. Divided into three comprehensive parts, this reliable resource offers a balance between the theoretical aspects of market microstructure and trading strategies that may be more relevant for practitioners. Along the way, it skillfully provides an informative overview of modern financial markets as well as an engaging assessment of the methods used in deriving and back-testing trading strategies. Details the modern financial markets for equities, foreign exchange, and fixed income Addresses the basics of market dynamics, including statistical distributions and volatility of returns Offers a summary of approaches used in technical analysis and statistical arbitrage as well as a more detailed description of trading performance criteria and back-testing strategies Includes two appendices that support the main material in the book If you're unprepared to enter today's markets you will underperform. But with Financial Markets and Trading as your guide, you'll quickly discover what it takes to make it in this competitive field.
Author: Darrell Duffie
Publisher: Princeton University Press
ISBN: 0691138966
Category : Business & Economics
Languages : en
Pages : 115
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Book Description
This book offers a concise introduction to OTC markets by explaining key conceptual issues and modeling techniques, and by providing readers with a foundation for more advanced subjects in this field.
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
ISBN: 1839216786
Category : Business & Economics
Languages : en
Pages : 822
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Book Description
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.
