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Author: Walter Mattli Publisher: ISBN: 0198829469 Category : Business & Economics Languages : en Pages : 385
Book Description
This book illustrates the dramatic recent transformations in capital markets worldwide. Market making by humans in centralized markets has been replaced by super computers and algorithms in often highly fragmented markets. This book discusses how this impacts public policy objectives and how market governance could be strengthened.
Author: Walter Mattli Publisher: ISBN: 0198829469 Category : Business & Economics Languages : en Pages : 385
Book Description
This book illustrates the dramatic recent transformations in capital markets worldwide. Market making by humans in centralized markets has been replaced by super computers and algorithms in often highly fragmented markets. This book discusses how this impacts public policy objectives and how market governance could be strengthened.
Author: Merritt B. Fox Publisher: Columbia University Press ISBN: 023154393X Category : Business & Economics Languages : en Pages : 612
Book Description
The U.S. stock market has been transformed over the last twenty-five years. Once a market in which human beings traded at human speeds, it is now an electronic market pervaded by algorithmic trading, conducted at speeds nearing that of light. High-frequency traders participate in a large portion of all transactions, and a significant minority of all trade occurs on alternative trading systems known as “dark pools.” These developments have been widely criticized, but there is no consensus on the best regulatory response to these dramatic changes. The New Stock Market offers a comprehensive new look at how these markets work, how they fail, and how they should be regulated. Merritt B. Fox, Lawrence R. Glosten, and Gabriel V. Rauterberg describe stock markets’ institutions and regulatory architecture. They draw on the informational paradigm of microstructure economics to highlight the crucial role of information asymmetries and adverse selection in explaining market behavior, while examining a wide variety of developments in market practices and participants. The result is a compelling account of the stock market’s regulatory framework, fundamental institutions, and economic dynamics, combined with an assessment of its various controversies. The New Stock Market covers a wide range of issues including the practices of high-frequency traders, insider trading, manipulation, short selling, broker-dealer practices, and trading venue fees and rebates. The book illuminates both the existing regulatory structure of our equity trading markets and how we can improve it.
Author: Martin Ebers Publisher: Cambridge University Press ISBN: 1108424821 Category : Computers Languages : en Pages : 321
Book Description
Exploring issues from big-data to robotics, this volume is the first to comprehensively examine the regulatory implications of AI technology.
Author: Álvaro Cartea Publisher: Cambridge University Press ISBN: 1316453650 Category : Mathematics Languages : en Pages : 360
Book Description
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you.
Author: Kendall Kim Publisher: Academic Press ISBN: 0080548865 Category : Computers Languages : en Pages : 224
Book Description
Electronic and algorithmic trading has become part of a mainstream response to buy-side traders' need to move large blocks of shares with minimum market impact in today's complex institutional trading environment. This book illustrates an overview of key providers in the marketplace. With electronic trading platforms becoming increasingly sophisticated, more cost effective measures handling larger order flow is becoming a reality. The higher reliance on electronic trading has had profound implications for vendors and users of information and trading products. Broker dealers providing solutions through their products are facing changes in their business models such as: relationships with sellside customers, relationships with buyside customers, the importance of broker neutrality, the role of direct market access, and the relationship with prime brokers. Electronic and Algorithmic Trading Technology: The Complete Guide is the ultimate guide to managers, institutional investors, broker dealers, and software vendors to better understand innovative technologies that can cut transaction costs, eliminate human error, boost trading efficiency and supplement productivity. As economic and regulatory pressures are driving financial institutions to seek efficiency gains by improving the quality of software systems, firms are devoting increasing amounts of financial and human capital to maintaining their competitive edge. This book is written to aid the management and development of IT systems for financial institutions. Although the book focuses on the securities industry, its solution framework can be applied to satisfy complex automation requirements within very different sectors of financial services – from payments and cash management, to insurance and securities. Electronic and Algorithmic Trading: The Complete Guide is geared toward all levels of technology, investment management and the financial service professionals responsible for developing and implementing cutting-edge technology. It outlines a complete framework for successfully building a software system that provides the functionalities required by the business model. It is revolutionary as the first guide to cover everything from the technologies to how to evaluate tools to best practices for IT management. - First book to address the hot topic of how systems can be designed to maximize the benefits of program and algorithmic trading - Outlines a complete framework for developing a software system that meets the needs of the firm's business model - Provides a robust system for making the build vs. buy decision based on business requirements
Author: Stefan Jansen Publisher: Packt Publishing Ltd ISBN: 1839216786 Category : Business & Economics Languages : en Pages : 822
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.
Author: Lukas Böffel Publisher: Springer Nature ISBN: 3031170776 Category : Business & Economics Languages : en Pages : 439
Book Description
This book covers three topics that have dominated financial market regulation and supervision debates: digital finance, sustainable finance, and the Banking and Capital Markets Union. Within the first part, seven chapters will tackle specific questions arising in digital finance, including but not limited to artificial intelligence, tokenisation, and international regulatory cooperation in digital financial services. The second part addresses one of humanity’s most pressing issues today: the climate crisis. The quest for sustainable finance is driven by political actors and a common understanding that climate change is a severe threat. As financial institutions are a cornerstone of human interaction, they are in the regulatory spotlight. The chapters explore sustainability in EU banking and insurance regulation, the interrelationship between systemic risk and sustainability, and the ‘greening’ of EU monetary policy. The third part analyses two projects that have led to huge structural changes in the European financial market architecture over the last decade: the European Banking Union and Capital Markets Union. This transformation has raised numerous legal questions that can only gradually be answered in all their intricacies. In four chapters, this book examines composite procedures, property rights of depositors in banking resolution, preemptive financing arrangements and the phenomenon of subsidiarisation in the context of Brexit. Of interest to academics, policymakers, practitioners, and students in the field of EU financial regulation, banking law, securities law, and regulatory law, this book offers a compilation of analyses on pressing banking and capital markets law problems.
Author: Robert Kissell Publisher: Academic Press ISBN: 0124016936 Category : Business & Economics Languages : en Pages : 492
Book Description
The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. - Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. - Helps readers design systems to manage algorithmic risk and dark pool uncertainty. - Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.
Author: Walter Mattli
Author: Walter Mattli
Author: Merritt B. Fox
Author: Martin Ebers
Author: Merritt B. Fox
Author: Gregory Scopino
Author: Álvaro Cartea
Author: Kendall Kim
Author: Stefan Jansen
Author: Lukas Böffel
Author: Robert Kissell