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 true meaning of investment discipline is to trade only when you rationally expect that you will achieve your desired objective. Accordingly, managers must thoroughly understand why they trade. Because trading is a zero-sum game, good investment discipline also requires that managers understand why their counterparties trade. This book surveys the many reasons why people trade and identifies the implications of the zero-sum game for investment discipline. It also identifies the origins of liquidity and thus of transaction costs, as well as when active investment strategies are profitable. The book then explains how managers must measure and control transaction costs to perform well. Electronic trading systems and electronic trading strategies now dominate trading in exchange markets throughout the world. The book identifies why speed is of such great importance to electronic traders, how they obtain it, and the trading strategies they use to exploit it. Finally, the book analyzes many issues associated with electronic trading that currently concern practitioners and regulators.

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.

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.

This book explores how the globalization of securities markets has affected market manipulation and insider trading. It delves into the responses of securities regulators, discussing new regulations designed to deter such misconduct, as well as they ways in which detection, investigation and prosecution techniques are adapting to tackle insider trading and market manipulation that crosses international boundaries.

The two volume proceedings of CCIS 698 and 699 constitutes revised selected papers from the 4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016, held in Hong Kong, China, in November 2016. The total of 118 papers presented in these proceedings were carefully reviewed and selected from 311 submissions. The contributions were organized in topical sections named: smart city in resource management and sustainable ecosystem; spatial data acquisition through RS and GIS in resource management and sustainable ecosystem; ecological and environmental data processing and management; advanced geospatial model and analysis for understanding ecological and environmental processes; applications of geo-informatics in resource management and sustainable ecosystem.

Identifying malpractice and misconduct should be top priority for financial risk managers today Corruption and Fraud in Financial Markets identifies potential issues surrounding all types of fraud, misconduct, price/volume manipulation and other forms of malpractice. Chapters cover detection, prevention and regulation of corruption and fraud within different financial markets. Written by experts at the forefront of finance and risk management, this book details the many practices that bring potentially devastating consequences, including insider trading, bribery, false disclosure, frontrunning, options backdating, and improper execution or broker-agency relationships. Informed but corrupt traders manipulate prices in dark pools run by investment banks, using anonymous deals to move prices in their own favour, extracting value from ordinary investors time and time again. Strategies such as wash, ladder and spoofing trades are rife, even on regulated exchanges – and in unregulated cryptocurrency exchanges one can even see these manipulative quotes happening real-time in the limit order book. More generally, financial market misconduct and fraud affects about 15 percent of publicly listed companies each year and the resulting fines can devastate an organisation's budget and initiate a tailspin from which it may never recover. This book gives you a deeper understanding of all these issues to help prevent you and your company from falling victim to unethical practices. Learn about the different types of corruption and fraud and where they may be hiding in your organisation Identify improper relationships and conflicts of interest before they become a problem Understand the regulations surrounding market misconduct, and how they affect your firm Prevent budget-breaking fines and other potentially catastrophic consequences Since the LIBOR scandal, many major banks have been fined billions of dollars for manipulation of prices, exchange rates and interest rates. Headline cases aside, misconduct and fraud is uncomfortably prevalent in a large number of financial firms; it can exist in a wide variety of forms, with practices in multiple departments, making self-governance complex. Corruption and Fraud in Financial Markets is a comprehensive guide to identifying and stopping potential problems before they reach the level of finable misconduct.