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

This book 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, g...

Seminar paper from the year 2003 in the subject Mathematics - Statistics, grade: 2.0 (B), European University Viadrina Frankfurt (Oder), language: English, abstract: Today the financial market becomes more complex and includes more competition. Reasons are trends like globalization, liberalization and lower-cost trading mechanism. The market microstructure research has the aim of an efficient market. It is focused on the structure of the financial market. The investigation becomes possible through the availability of high- frequency data. Those data exist especially in the United States and like that most of the research focuses this market. To explain the phenomena, which have been found adequate, models that fit the characteristics of high- frequency data have to be developed. The research is important to understand actions on the market as well as develop new efficient mechanism. One part of the market microstructure field is the bid-ask spread. It will be focus of this paper. In the first two parts it will be discussed theoretically. In the last part one model will be empirically analyzed and tested on its usefulness and validity. The second part of this paper explains the basic elements surrounding the research of bid-ask spread. Those are the financial market, market microstructure as well as high-frequency data. In the following part the bid-ask spread itself, approaches, researches and models focussing the spread will be discussed. The model of Roll (1984) will be explained in detail. The last part will be the empirical analysis of the model of Roll. It is analyzed with data from the NASDAQ.

This book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners.

John J. Murphy has updated his landmark bestseller Technical Analysis of the Futures Markets, to include all of the financial markets. This outstanding reference has already taught thousands of traders the concepts of technical analysis and their application in the futures and stock markets. Covering the latest developments in computer technology, technical tools, and indicators, the second edition features new material on candlestick charting, intermarket relationships, stocks and stock rotation, plus state-of-the-art examples and figures. From how to read charts to understanding indicators and the crucial role technical analysis plays in investing, readers gain a thorough and accessible overview of the field of technical analysis, with a special emphasis on futures markets. Revised and expanded for the demands of today's financial world, this book is essential reading for anyone interested in tracking and analyzing market behavior.

Traders who understand the statistics and probabilities behind the movements of financial markets have to tools to find an enduring trading edge. This book is written to be accessible to the trader without a heavy mathematical background, and works toward a deep, intuitive understanding of statistical tools applied to market data. Avoid common missteps and develop the skills to put the odds in your favor.

In part 1, Carol Alexander brings many new insights to the pricing and hedging of options with her understanding of volatility and correlation, and the uncertainty which surrounds these key determinants of option portfolio risk. Modelling the market risk of portfolios is covered in part 2 where the main focus is on a linear algebraic approach; the covariance matrix and principal component analysis are developed as key tools for the analysis of financial systems. The traditional time series econometric approach is explained in part 3, with coverage ranging from the application cointegration to long-short equity hedge funds, to high-frequency data prediction using neural networks and nearest neighbour algorithms . Throughout this text the emphasis is on understanding concepts and implementing solutions. It has been designed to be accessible to a very wide audience: the coverage is comprehensive and complete and the technical appendix makes the book largely self-contained.