Developing an Effective Model for Detecting Trade-Based Market Manipulation determines an appropriate model to help identify stocks witnessing activities that are indicative of potential manipulation through three separate but related studies.

The economic theories that dominated the field during the 20th century have failed us and empowered government to believe they can manipulate the business cycle. Every economic theory presented post-Marxism has assumed that the complexity of the business cycle can be reduced to a single cause and effect. To date, no attempt to manipulate the cycle has prevented a recession or financial crisis. We now face a truly monumental crisis. Central banks around the world are trapped. Their attempt to stimulate the economy through Quantitative Easing and rate manipulation has disastrously failed. The central banks have primarily purchased government debt, effectively keeping governments on life support by allowing them to issue new debt at substantially lower rates. In addition to catastrophic Quantitative Easing policies, political fiscal spending on various programs and agencies has burdened governments with a debt that they can never repay. The future crisis is one created by government. This time, we are not likely to fix the problem without major political reform, which all governments will resist. These policies have led many to assume that government can freely create money without inflation. After creating trillions of dollars to buy government debt with no appreciable inflation, many conclude that everything has changed. They are calling this the Modern Monetary Theory. If they are correct, then why bother to have taxes or borrow money continuously with no intention of paying off national debts? Governments, in modern theory, can simply create an endless supply of money to create a new modern version of Utopia. Can we throw away all economic history for an experiment that could unravel civilization if the theory proves to be wrong? What are the risks? Can it really be that easy? Are there any examples from the past that we can look to for answers?

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.

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.

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading strategies Key Features Understand the power of algorithmic trading in financial markets with real-world examples Get up and running with the algorithms used to carry out algorithmic trading Learn to build your own algorithmic trading robots which require no human intervention Book Description It's now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate. You'll start with an introduction to algorithmic trading, along with setting up the environment required to perform the tasks in the book. You'll explore the key components of an algorithmic trading business and aspects you'll need to take into account before starting an automated trading project. Next, you'll focus on designing, building and operating the components required for developing a practical and profitable algorithmic trading business. Later, you'll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you'll create a trading bot from scratch using the algorithms built in the previous sections. By the end of this book, you'll be well-versed with electronic trading markets and have learned to implement, evaluate and safely operate algorithmic trading strategies in live markets. What you will learn Understand the components of modern algorithmic trading systems and strategies Apply machine learning in algorithmic trading signals and strategies using Python Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more Quantify and build a risk management system for Python trading strategies Build a backtester to run simulated trading strategies for improving the performance of your trading bot Deploy and incorporate trading strategies in the live market to maintain and improve profitability Who this book is for This book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.

The era of globalization saw China emerge as the world's manufacturing titan. However, the "made in China" model—with its reliance on cheap labor and thin profits—has begun to wane. Beginning in the 2000s, the Chinese state shifted from attracting foreign investment to promoting the technological competitiveness of domestic firms. This shift caused tensions between winners and losers, leading local bureaucrats to compete for resources in government budget, funding, and tax breaks. While bureaucrats successfully built coalitions to motivate businesses to upgrade in some cities, in others, vested interests within the government deprived businesses of developmental resources and left them in a desperate race to the bottom. In Manipulating Globalization, Ling Chen argues that the roots of coalitional variation lie in the type of foreign firms with which local governments forged alliances. Cities that initially attracted large global firms with a significant share of exports were more likely to experience manipulation from vested interests down the road compared to those that attracted smaller foreign firms. The book develops the argument with in-depth interviews and tests it with quantitative data across hundreds of Chinese cities and thousands of firms. Chen advances a new theory of economic policies in authoritarian regimes and informs debates about the nature of Chinese capitalism. Her findings shed light on state-led development and coalition formation in other emerging economies that comprise the new "globalized" generation.